The confusion matrices showing the classification accuracy obtained by applying “HE-VAR and PAN”, “MCLBP and VAR” and “Proposed” methods separately on IKONOS image shown in Figure 1a.
\r\n\t
",isbn:"978-1-83968-076-2",printIsbn:"978-1-83968-075-5",pdfIsbn:"978-1-83968-080-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"456f82c97eafad5734cd36c48e167781",bookSignature:"Dr. Redmond Ramin Shamshiri",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10499.jpg",keywords:"Microclimate Control, Prediction Models, Environment Monitoring, Computer Models, Cloud Computing, IoT Monitoring, Simulink, Solar Greenhouses, Exergy, Urban Greenhouses, Virtual Crop Production, Artificial Lighting",numberOfDownloads:67,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 1st 2020",dateEndSecondStepPublish:"July 22nd 2020",dateEndThirdStepPublish:"September 20th 2020",dateEndFourthStepPublish:"December 9th 2020",dateEndFifthStepPublish:"February 7th 2021",remainingDaysToSecondStep:"7 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Shamshiri is the Member of the International Society of Precision Agriculture and Member of the American Society of Agricultural and Biological Engineering. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"55598",title:"A Less Attractive Feature of Empathy: Intergroup Empathy Bias",doi:"10.5772/intechopen.69287",slug:"a-less-attractive-feature-of-empathy-intergroup-empathy-bias",body:'\nHumans are ultra‐social organisms, because they form and depend on organizations that extend beyond the individual [1]. This social interdependence arguably evolved because group living, and its associated social functions, offered several reproductive and long‐term survival advantages, compared to going solo [2]. In turn, these social functions necessitated the evolution of more sophisticated cognitive and emotional capacities, like theory of mind and moral emotions, to enable thriving within a group context [3]. Critical among these is our ability to empathize: the capacity to think and feel oneself into the inner reality of another person, while recognizing that their emotional experience is separate from our own [4]. Empathizing with others in distress is particularly important because it motivates behavior aimed at alleviating those others’ suffering, and this, in turn, promotes social cohesion and resource‐sharing among members of society [5, 6].
\nUnfortunately, witnessing a person in distress does not inevitably evoke feelings of empathy, nor does it always result in prosocial helping behavior. Even though we may encounter many potential empathy‐eliciting scenarios in our everyday lives, we respond with empathy to only a fraction of them [7, 8]. In fact, recent evidence suggests that empathic failures are not always characterized by attenuated empathy or indifference, but quite often by counter‐empathic responses, like Schadenfreude and Glückschmerz, which may facilitate hostility [9, 10]. Empathic reactions are therefore not automatic, but rather, the degree to which we respond empathically are modulated by multiple interlocking factors, which science is only beginning to unravel. For example, growing evidence suggests that empathic responding is influenced significantly by personal features of the empathizer (e.g., gender, trait empathy, childhood trauma), by interpersonal factors (e.g., perceived fairness, social stigma), by cultural factors (e.g., interdependence vs. independence, preference for social hierarchy), and importantly, by the social group membership of the person in distress (e.g., race, political affiliation, sports team identification) [11–18]. Although empathy for one’s own social group is particularly important and holds several long‐term advantages [19], the flipside of this phenomenon, i.e., diminished empathy for the outgroup, poses profound challenges for our modern human world where a multitude of groups, ethnicities, and cultures compete for the same resources [20].
\nThis chapter explores a fundamentally important, albeit less attractive, feature of empathy, namely, its breakdown in response to the pain of outgroupothers. In particular, it asks the following questions: Where and how does the empathic process break down? Which factors exacerbate this empathic failure? And is it possible to ameliorate, or even reverse, these effects? The chapter frames the discussion by contextualizing empathy as consisting of three interacting component processes, namely, experience sharing, perspective taking, and empathic concern. It then goes on to examine research describing the effects of intergroup bias on each of these component processes. Next, it explores the factors, both at the level of the group and at the level of the individual, which may contribute to empathic breakdown in intergroup contexts. Finally, it considers strategies that may have potential in mitigating intergroup empathy bias.
\nEmpathy is a complex psychological phenomenon. Whereas early conceptualizations of empathy have typically stressed either its cognitive or affective aspects [21], more recent research considers empathy a multidimensional construct that may be parsed into three dissociable but interacting neurocognitive components: emotional sharing, perspective taking, and empathic concern [22, 23].
\nEmotional sharing constitutes the affective component of empathy and is also commonly referred to as emotion contagion or affective resonance. It refers to the automatic capacity to become aroused by another’s emotions and relies on subcortical emotion circuits [7]. For example, seeing an angered individual may lead the empathic observer to vicariously experience similar feelings.
\nPerspective taking constitutes the cognitive component of empathy and refers to the capacity to view a situation from another’s point of view, or to put oneself in their shoes, as it were. Doing this allows one to better recognize and understand another person’s affective experience. It therefore partly overlaps with theory of mind‐like processing and relies on the mentalizing network [24, 25]. Because perspective taking begins with the perception of affective signals from another individual, low‐level processes involved in face perception and emotion recognition are integral to understanding another individual’s internal state [26].
\nFinally, empathic concern constitutes the motivational component of empathy and refers to other‐oriented feelings of care and compassion when perceiving an individual in distress. The experience of empathic concern may stimulate prosocial helping or caring behaviors in the observer [5, 27]. Recent neuroimaging studies have highlighted the role of the medial prefrontal cortex (MPFC) [13] and septal area [25] in empathy‐driven prosocial motivation.
\nEach empathic component is sensitive to both bottom‐up automatic affective processes and to top‐down perceiver‐controlled cognitive processes, which feed into each other [7]. For example, too much affective arousal may result in personal distress within the observer, promoting a self‐focused escape response rather than other‐oriented empathic response [28]. In turn, executive functions serve to consciously regulate the self‐focused distress response, so that an awareness of the other’s perspective and concern for their circumstances becomes possible [29]. These interconnected mechanisms function together to produce an overall empathic response that combines automatic experience sharing as well as intentional feelings of concern.
\nAbundant evidence from social psychology suggests that individuals instinctively categorize themselves into salient social groups with which they identify and with whom they feel a sense of belonging [30, 31]. Unfortunately, this categorization also maximizes differences between groups, leading people to more readily distrust, fear, and discriminate against outgroup members and to instinctively favor or side with members of social groups with which they identify [32].
\nImportantly, this intergroup bias has been shown to impact emotional responding toward others when it matters most—when they are in pain. Whereas greater empathy toward one’s ingroup makes sense from an evolutionary perspective [20], empathic failures toward outgroup members may lie at the heart of most intergroup conflict situations, including political violence, xenophobia, and genocide [33]. Interestingly, outgroup empathy failures do not seem to depend on a person’s trait empathic concern [34]. That is, even the most deeply empathic person can mute their empathic response toward a perceived enemy under the right circumstances—a phenomenon that has been referred to as the mind’s “empathy gap” [35]. Although the mechanism that underlies this empathy gap remains unclear, studies investigating intergroup empathy have demonstrated that outgroup membership status can compromise all levels of empathic responding (i.e., affective, cognitive, and motivational), as well as helping behavior [18, 36–38].
\nAn important aspect of intergroup bias is the fact that it appears to depend heavily on the perceiver’s social motivation: various studies have shown that self‐categorization broadly along some ingroup/outgroup distinction is flexible and that re‐categorization with an arbitrarily defined group may be sufficient to override automatic response biases [39]. In these studies, participants are typically assigned to novel groups using a minimal group paradigm [40] and then subjected to a variety of tasks assessing perceptual, affective, and behavioral ingroup biases [41–44]. Recently, researchers have specifically advocated the advantages of assigning people to novel groups, rather than focusing on specific social or historic groups, to advance our understanding of the processes that guide intergroup behavior across multiple contexts and levels of analysis [45].
\nWhen it comes to empathic responding more specifically, arbitrary group categorization by way of a minimal group manipulation can also facilitate intergroup biases, particularly when the groups are in competition [9]. For example, previous research found that similar group membership between a helper and target (regardless of whether the group was real or artificially determined) strengthened the role of empathy and helping [46]. Similarly, mere categorization of participants into non‐relevant social groups appears sufficient to facilitate an ingroup bias in empathy for physical pain [47].
\nNot many studies have explored the relationship between race and minimal group biases in empathic responding, however, and results from these studies are inconsistent. For example, in a recent study, participants showed clear minimal group biases (unaffected by race) on both an explicit group identification and implicit affective priming task, whereas neural imaging responses were indicative of significantly greater empathic arousal in response to own‐race compared to other‐race individuals [48]. The authors concluded that racial categorization may be a stronger modulator of the ingroup bias in empathic neural processing than general social group categorization (i.e., by assigning participants into random teams). One possible reason for this finding is that humans may (automatically) detect and encode race as a by‐product of an adaptation to identify fellow group members [49, 50]. Given that findings to date have been inconclusive, however, more research will be necessary to tease apart these effects (see also [51]).
\nEmotional sharing is important in the context of empathy, because it plays a fundamental role in generating the motivation to care for and help another individual in distress [52]. Despite a general notion that affective arousal is automatic, empirical evidence suggests that many variables, including a priori attitudes and culturally learned associations, affect its induction in the observer (see e.g. [53, 54]). Notably, reduced affective resonance when viewing an outgroup member in pain may be associated with fewer physiological signals from the observer’s body to help interpret the other individual’s emotional state and stimulate prosocial action [26].
\nVarious studies exploring affective resonance in response to in‐ and outgroup members in physical pain found dampened autonomic arousal in response to outgroup members’ pain [17, 37]. Interestingly, in these studies, greater levels of racial prejudice on implicit measures of prejudice were associated with greater lack of empathic arousal toward outgroup members. Reduced emotional sharing in response to outgroup members is not exclusive to scenarios of physical pain; however, it also extends to scenarios of emotional pain. For example, Gutsell and Inzlicht [55] recorded electroencephalographic (EEG) alpha oscillations when participants observed ingroup and outgroup members expressing sadness. They found that, whereas participants showed similar activation patterns when experiencing sadness themselves and when observing ingroup members feeling sad, participants did not show these same vicarious activation patterns when observing outgroup members feeling sad. Participants thus appeared to experience reduced emotional sharing in response to outgroup members’ sadness, and this became more pronounced the more prejudiced they were.
\nSeveral neuroimaging studies have shown that intergroup empathy bias may also manifest as increased hemodynamic activation in the anterior cingulate cortex (ACC) and anterior insula—areas thought to underlie the subjective representation of affective distress [18, 56, 57]. Notably, in a recent study, we detected significantly enhanced amygdala activity in response to own‐race compared to other‐race individuals in perceived physical pain [58]. The amygdala is typically activated during events high in emotional salience or novelty and may direct attention to motivationally relevant stimuli [59]. We thus argued that heightened amygdala activation toward same‐race individuals in pain reflects approach‐related motivation and attention in line with task demands, which urged participants to empathize with individuals in distress [39].
\nAt the level of emotion recognition, research suggests that observers are faster and more accurate at distinguishing own‐race compared to other‐races faces [60] and are better at identifying emotional expressions of racial ingroup compared to outgroup members [54, 61]. Research has also found that people are less likely to attribute secondary emotions, which are uniquely human characteristics, to outgroup compared to ingroup members [38, 62].
\nWhen it comes to perspective taking, people appear to be more likely, or more accurate, in taking the perspective of an ingroup member compared to an outgroup member. For example, when Asian and White participants viewed photographs of members of both racial groups in negative contexts (e.g., illness, grief, injury) and positive contexts (e.g., party, amusement, smiling), participant self‐report data indicated greater perspective taking and empathy for own‐race members than other‐race members, particularly in the negative contexts [36]. Furthermore, several neuroimaging studies, including our own, demonstrated stronger hemodynamic activation in response to others’ pain for racial ingroups versus outgroups in regions associated with mentalizing, including the medial prefrontal cortex (MPFC), temporoparietal junction (TPJ), and precuneus [12, 58, 63]. In our study, we concluded that, because these areas form part of networks implicated in self‐referential processing, episodic memory retrieval, and thinking about other minds, heightened activity may allow for a richer representation of another’s physical/psychological pain. By implication, reduced activity in these areas suggests impaired perspective taking when it comes to outgroup members.
\nPerspective taking is thought to enhance empathy by creating increased overlap between “self” and “other” cognitive representations, thereby reducing the self‐other gap [64]. Merging another individual into one’s self‐concept thus results in a feeling of “oneness” and a sense of shared identity with the other person, which facilitates understanding someone else’s emotions as if they were one’s own. Unfortunately, people experience self‐other merging much more easily with those whom they perceive as more similar to themselves, such as family members and close friends [65].
\nVarious lines of evidence suggest that empathic concern and resulting helping behaviors are affected by intergroup biases. For example, Drwecki et al. [66] found that White participants reported greater empathic concern for White individuals in pain than for Black individuals in pain, and offered higher levels of analgesic treatment for ingroup compared to outgroup members. Also, in two neuroimaging studies, empathy‐related neural activity when observing ingroup members’ suffering relative to outgroup members’ suffering predicted greater willingness to donate time and money [63], as well as greater costly helping [18] for ingroup members at a later stage.
\nThe literature on helping behavior is complex, however, and a variety of factors may influence one’s decision to offer help, including altruistic motivation, a sense of similarity with the outgroup, self‐regulatory depletion, and competitive or status‐related processes [26, 67]. A quantitative meta‐analysis of White individuals’ helping directed toward Black individuals indicated no overall tendency to discriminate against racial outgroup members [68]. Instead, White individuals tended to help Black and White individuals equally, except when helping required considerable effort, time, or risk, which then resulted in an ingroup bias in helping. Broadly speaking, the literature suggests that when helping requires greater resources, individuals may cognitively justify not helping by basing their decision on reasons other than the (racial) outgroup of the person in need. Although helping ingroup members may thus be motivated largely by empathic concern, helping outgroup members may involve further systematic decision‐making based on the costs and benefits of offering help [46].
\nIn recent years, at least two things have become increasingly apparent in intergroup research: social group membership is highly flexible and context‐dependent, and not all outgroups elicit intergroup empathy bias equally. In trying to tease apart the complex array of factors that influence intergroup responding—at least at the level of the group—Cikara and van Bavel [45] have recently argued that two factors are critical: functional relations between groups (shared, competing, or independent goals) and relative group status (access to resources).
\nEmpathic breakdown between members of rival or conflict groups is well documented [69–71]. People’s relationships to others play a significant role in determining how they respond to their suffering: whereas a friend’s misfortune typically elicits empathy, a foe’s misfortune might be experienced as less distressing, or even as pleasurable [72, 73]. Unfortunately, intergroup contexts tend to exacerbate people’s motivation not to empathize or care about someone else’s misfortune, in that groups may provoke significantly more competition and aggression than interpersonal interactions [10, 74].
\nExplicit competition between groups has the effect of increasing the salience of social identity and generally strengthens the positive relationship between ingroup identification and intergroup bias and hostility [31, 75]. For example, in a neuroimaging study of real‐world sports rivalry between avid fans of the Red Sox and Yankees baseball teams, ingroup team failures were associated with increased activity in neural areas associated with the subjective experience of pain [76]. In contrast, outgroup team failures were associated with increased self‐reported pleasure and activity in neural areas associated with reward processing. Moreover, and rather disturbingly, the more positive value (pleasure) participants attached to rival team failures, the more they were willing to aggress against a fan of the rival team. Similar results were also observed when novel groups were pitted against each other [9]. Of significance is that in the latter study, intergroup empathy bias between competing groups was robust beyond contexts that defined the groups themselves, and even when the competitive threat of the outgroup was removed (e.g., feedback that the outgroup has fallen behind).
\nA second factor central to intergroup dynamics concerns the question of resources: To what extent does a social group have the power to carry out their intentions? [45]. Groups higher up the social hierarchy have more status and greater access to resources, and thus greater potential threat value, whereas groups lower down the hierarchy are typically scorned and pose less of a threat [77]. Even without overt competition, differences in power and resources between groups have been shown to predict perceptions of competitiveness [78]. Importantly, historical asymmetries in power and status between groups affect intergroup empathic responding, as well as lower level perceptual processes that operate outside awareness [79].
\nIn an interesting study assessing people’s perceptions of Black and White pain, the authors detected a consistent racial bias in evaluations, such that Black people were consistently perceived to experience less pain than White people [80]. Crucially, this bias in pain perception (by both Black and White Americans, including nursing professionals) could not be attributed to racial prejudice, but instead appeared rooted in perceptions of status and the privilege or hardship it confers. Hence, the less privileged a target seemed, the less pain participants thought he/she would experience. In a similar vein, another study using facial electromyography showed that an individual’s relative social status affects how other people respond to their misfortune: participants felt less bad and smiled more when negative events happened to high‐status compared to low‐status individuals [81].
\nAlthough group membership significantly impacts empathic responding, empathy is not solely influenced by external factors, such as the race or status of the person in distress. Individual differences of the perceiver may also moderate the extent to which there is an intergroup bias in empathic responding. That is, based on individual traits, some people might be more likely to show strong intergroup biases in empathic responding than others.
\nEmpirical studies show that the strength of racial identification may contribute to intergroup bias in empathic responding [63, 82]. Although social group membership defined according to race is a prominent aspect of interactions among individuals, the extent to which people identify with their own racial group varies from person to person [83]. Some individuals regard their racial identity as a crucial part of their self‐concept, whereas others may not feel a strong belonging to their racial group. Strong racial identification makes it more likely that an individual will process the emotions of racial ingroup members in a self‐referential manner, resulting in greater empathy toward own‐race individuals. Interestingly, some research suggests that pervasive discrimination against members of disadvantaged groups is associated with increased ingroup identification, which, in turn, may alleviate some of the negative psychological consequences of societal dehumanization [84, 85].
\nAnother important factor that contributes to variation in intergroup relations is motivation to respond without prejudice [86]. Because overt racial discrimination is not socially acceptable, society generally favors individuals who act in non‐prejudiced ways. Hence, individuals are motivated to alter their behavior to appear non‐prejudiced. The strength of the motivations to respond without prejudice, and the extent to which these motivations influence behavior, however, vary between individuals [87]. Furthermore, people may be motivated primarily by sincere changes in their personal attitude (internally motivated; IMS), or they may be motivated primarily by external pressures to avoid judgment or punishment from others (externally motivated; EMS) [88].
\nData from our research have shown that different motivations to appear non‐prejudiced can modulate intergroup empathic responding. For example, in a functional magnetic resonance imaging (fMRI) study of intergroup empathy, higher EMS scores in White participants were associated with dampened neural empathic responses toward Black individuals in both physical and emotional distress [58]. And in another study, higher IMS scores in White participants were positively associated with prosocial helping behavior toward a Black individual in distress, whereas higher EMS scores were negatively associated with prosocial helping toward that individual [89].
\nIn the introductory section of this chapter, we presented an evolutionary perspective as framework for understanding the social interdependence of human beings that is foundational in the development of empathy. In presenting strategies to reduce intergroup empathy bias, we draw on theoretical formulations from different branches of psychology regarding the development of empathy in intergroup contexts. Notably, various studies in social psychology have demonstrated the efficacy of cognitive strategies (e.g., increased attention to an individual’s feelings vs. his/her group status), manipulating the intergroup relationship (e.g., cooperative vs. competitive), manipulating group membership (e.g., recategorizing or decategorizing individuals), blurring group boundaries (e.g., reducing perceptions of group entitativity), and effortful perspective taking, in reducing intergroup empathy bias [9, 51, 90–92]. While success in reducing intergroup empathy bias along these lines is thus possible, results of such strategies appear to be highly context dependent [93]. Moreover, investigators have rarely evaluated the efficacy of the strategy they employed beyond the immediate study context or longitudinally [94].
\nScholars influenced by the relational psychoanalytic discipline have often followed a different approach in restoring empathic bonds, observing that the need and proclivity for connection are central to human development [95]. Stolorow and Atwood, for example, have argued for the primacy of interconnectedness and advanced the theory of intersubjectivity—a development of self, understood in interaction with others [96]. Accordingly, connection with others is fundamental in the development of one’s identity, and experience and subjectivity are shaped by these relationships with others. The intersubjective epistemological model provides an important conceptual guideline for understanding the deeper significance of processes of perspective taking that unfold in intergroup encounters, which may, in turn, lead to the strengthening of empathic bonds.
\nSpecifically, the subtleties of the dynamic at play in dialogic intergroup encounters are shaped by the reciprocal influence and mutual awareness that develop in the intersubjective field—created in the coming together of people from two different groups representing two different historical perspectives. Thus, through a process of genuine listening to the other’s story and pain in a facilitated, interactive process, the resonance that unfolds opens up the possibility for individual participants from each side to enter into the feeling state of the other. It is in this intersubjective engagement with the other’s story that the emergence of shared empathy becomes possible.
\nWe have referred to this unfolding process as “empathic repair” [69], a process of intersubjective repair that points to a deeper level of mutual recognition, one that occurs both intrapsychically and in the participants’ external world through expressions of acknowledgment. This mutuality of a shared transformative moment is the fundamental moment of empathic repair and reciprocal recognition of the other’s humanity that creates pathways to caring for the other as a fellow human being. In the following section, we describe apartheid South Africa as a concrete example of intergroup empathy failure and how dialogue processes initiated by the Truth and Reconciliation Commission opened up possibilities for empathic connection.
\nOur interest in questions of empathic failures, and how empathic connections between groups might be enhanced, grew out of our work in the South African context with its history of intergenerational mistrust, hatred, and resentment born out of the violence of policies of apartheid. Both physical violence and the kind of violence that results from a lifetime of humiliation, passed down across generations of oppressed groups, create boundaries and deep divisions in relationships between self and others. This means that starting well before one’s capacity even to make moral choices has been tested, one’s sense of moral obligation toward others is rigidly channeled along lines of “us” versus “them,” and the images of “them” depict a group that exists only as objectified others. Empathic failure operates under these conditions of a deep separation between racial groups as codified in apartheid laws.
\nAs a strategy to find a sustainable way of dealing with these failures of empathy, and under Nelson Mandela’s leadership, the Truth and Reconciliation Commission (TRC) was introduced in South Africa. What happened at the TRC may not be generalizable to all other post‐conflict contexts. But what the work of the TRC has shown is that empathic connection between former adversaries can indeed be restored. An important condition for this to happen is the forging of dialogue and a vocabulary of compromise and tolerance, because the exercise of forming that vocabulary involvessettling differences through the politics of contestation and compromise among people separated by laws based on intergroup hatred.
\nSouth Africa’s TRC, with its remarkable stories of forgiveness and healing, was a powerful illustration of how under certain conditions, instead of widening boundaries and deepening empathic failures, post‐conflict dialogue processes can facilitate genuine connection in an intergroup context. Dialogue creates the possibility of setting the actions of “the other” in the broader framework of the political‐ideological context that may have supported, and even directed, the hateful acts that excluded one from the moral and empathic obligations of “the other.” Thus, the politics of abuse that were enshrined in the policies of an oppressive system such as apartheid could be acknowledged and confirmed in ways that opened up the possibility for the emergence of empathy between former adversaries.
\nFreud remarked that empathy “plays the largest part of our understanding of what is inherently foreign to our ego in other people” [97]. It is therefore not surprising that situations where empathy for another’s distress is absent or reduced are very often also characterized by distrust, hatred, violence, discrimination, and even pleasure. The current chapter explored the complexities of intergroup empathic responding in an effort to gain a better understanding of the mechanisms that govern this process. First, we have shown that group membership affects all levels of empathic responding: experience sharing, perspective taking, and empathic concern. Second, we have pointed out the fluid nature of groups, in that both functional relations and differences in power and status may affect intergroup empathic responding at any given time. In addition, we have shown how various individual difference characteristics, notably racial identification and motivations to respond without prejudice, can influence intergroup empathic responding.
\nThe intergroup landscape is not universally bleak, however, and empathic response differences across social categories are not inevitable. Drawing on our experiences in post‐apartheid South Africa, we believe the intersubjective space that unfolds between former adversaries when coming together in facilitated dialogue with each other opens up rich possibilities for a shared empathy and mutual recognition of the other’s humanity. These connections create new relational experiences that can help restore historical ruptures.
\nThis work was supported by the National Research Foundation (NRF) of South Africa and Stellenbosch University (SU).
\nHigh spatial resolution remotely sensed imagery helps to obtain quality and detailed information about the earth’s surface features in conjunction with their geographical associations. The internal changeability within the identical land-use land-cover units augments with the rise of resolution. The augmented changeability diminishes the statistical distinguishability of land-use/land-cover classes in the spectral data space. This reduced distinguishability tends to decrease the accuracies of pixel-based clustering algorithms such as Fuzzy C Means [1], minimum distance classifiers [2] and K-Means [3]. These pixel-based clustering techniques assign a pixel to a region according to the similarities of spectral signature. It considers only one pixel at a time [4]. Spectral signatures are the specific combination of emitted, reflected or absorbed electromagnetic (EM) radiation at varying wavelengths which can uniquely identify an object [4].
Compared to IRS-1A/1B sensors, the spectral resolution of high spatial resolution images is normally relatively poor. Spectral resolution describes a sensor’s ability to identify fine intervals of wavelength. The better the spectral resolution, the finer the channel or band width. Therefore, between spatial and spectral resolution, there is a trade-off. It is mainly true for panchromatic (PAN) images of high spatial resolution, namely CARTOSAT-II 1m and IKONOS 1m. There is a need to consider the spatial relationships between pixel values, also known as the ‘texture’ of the scene objects to classify high-resolution (HR) images owing to the wide difference in the spatial structure in these images. Consequently, multiple texture-based clustering technique namely GLCM [5, 6, 7, 8], Markov random field (MRF) model [5], Gray scale rotation invariant [9] were evolved for clustering remote sensing images having high spatial resolution. Nevertheless, above mentioned methods are appropriate in textured area of HR images. A region is called textured; where the intensity dissimilarity within adjacent pixels is substantial. A region is said to be non-textured, where the intensity dissimilarity among adjacent pixels is insignificant [10, 11]. But texture-based classification techniques failed in non-textured region of high spatial resolution image as much variation is not found in the spatial pattern of those regions of the image [12]. Thus, we can infer from earlier studies that classification of high spatial resolution imageries either by pixel or texture-based algorithm may not yield desired results.
Some more techniques namely watershed approach [13, 14], region-growing approach [4, 15], mean shift approach [16, 17], region merging approach [18] etc. are in use for clustering high spatial resolution remote sensing images. Application of these approaches for clustering of images either leads to under-segmentation or over-segmentation [19, 20]. Structural image indexing approach [21], semi-supervised feature learning approach [22] and multi-scale manner using SVM approach [23] are also found fairly suitable in clustering high resolution images. The imagery of higher resolution includes textured and non-textured areas. Hence, pixel or texture-based algorithm for clustering of high-resolution imagery does not produce expected results. This type of high-resolution imagery clustering research is in the trend. Multi-circular local binary pattern and variance-based method [10] were used separately to cluster high resolution image having textured and non-textured regions. The Multi circular local binary pattern operator has been used here for measuring the spatial structure of the image. But, disadvantage in this strategy is that multi-circular local binary pattern operator is susceptible to noise as it exactly sees the value of the moving window’s central pixel as a limit for computing the spatial structure around the central pixel.
In last one decade the Hölder exponent (HE) has been used for calculating spatial structure of the images [24, 25, 26]. It is also being used for clustering high-resolution images [12]. HE gives an evidence of the spatial structure of the image and is not much influenced by the noise. In addition, spatial structure, contrast of the local image holds considerable property for calculating the texture around the pixel. In this research, high-resolution picture textured and non-textured region is originally segmented using HE and VAR-based method and subsequently separately clustered and non-textured areas. VAR is used to calculate the contrast around the pixel. The suggested method is applied with a 1 m spatial resolution on high resolution IKONOS PAN images.
The suggested high-resolution image ‘P’ clustering technique has three main steps: (i) image transformation, (ii) segmentation and extraction, and (iii) clustering. Initially, every pixel of the image is converted into a degree of texture or non-texture around the pixel. In the second step, using segmented image mask, the transformed image is segmented and non-textured and textured regions are extracted from the initial image. Finally, the two areas obtained are separately clustered.
The Hölder Exponent (HE) and VAR are jointly used to convert the image for computing the texture. The HE calculates each pixel of P’s spatial structure. Besides spatial structure, local image contrast also grasps important property for computing the texture around the pixel. In this research, therefore, VAR is used to calculate the contrast around the pixel.
Hölder exponent has been used for investigating the texture in high-resolution images [12]. It measures the irregularity in the vicinity. Supremacy of applying Hölder Exponent in HR images are that (i) it can be used as an instrument to calculate each pixel of the image’s spatial structure, (ii) no previous data on the pixel intensity is required and (iii) is not very sensitive to noise [12].
Definition of HE [27]: Let μ be a measure on a set Ω as well as for all x Є Ω, э α(x), such that μ (Br(x)) ∼ rα, for small r. Here Br(x) is circle (2D) of radius i centered on x. Then α (x) is called the HE on x.
A sequence of 15 values of radius r (i.e. 1, √2, √5, 3, √13, 3√2, 5, √29, 2√10, 3√5, 7, √61, 6√2, √85, 7√2) centered on x are used as a scale parameter for calculating HE value around each pixel x in the image [12] and the total number (N) of intersected pixels by the perimeter of series of circles of radius r is considered as a scale parameter for computing VAR value around x [12]. N is computed using Eq. (1).
where t is the total number of identified circles, mr is the number of intersected pixels on the perimeter of the radius r circle.
To get the contrast value of (x, y), the neighbor’s σ2 of each pixel (x, y) is calculated over the entire image. Using Eq. (2), the σ2 (x, y) is realized
where arj is the intensity value of pixel (r,j),
Thus obtained α(x,y) and σ2 (x,y) for each P(x,y). Afterward, these values are used in Eq. (3) to obtain the corresponding pixel value (x,y) in the transformed image T. Each pixel (x,y) of T signifies the degree of texture around that pixel.
The image ‘T’ is segmented into textured and non-textured regions based on a threshold value ‘δ’. The pixel value in T below the ‘δ’ is considered to be a non-textured region, whereas greater than or equal to ‘δ’ is considered to be the textured region in the segmented image. Pixels are labeled as zero in non-textured areas, whereas pixels are marked as one in textured areas in the segmented image mask and depicted as follows:
where T(x,y) and Γ(x,y) represents the pixel value in (x,y) position of the two dimensional transformed image and segmented image respectively and δ represents the threshold value. The δ is calculated by using Eq. (5).
where Tmin and Tmax represents minimum and maximum pixel gray value in T respectively and K is user defined value.
IKONOS PAN sensor image of size 256 × 256 pixels (shown in Figure 1a) is used to achieve the optimum K. The suggested clustering method is also implemented for distinct K values on this image.
(a) IKONOS image showing vegetation, built-up area, fallow and water body categories, (b) classified image obtained by applying “HE-VAR and PAN” based method on Figure 1a, (c) classified image obtained by applying “MCLBP and VAR” based method on Figure 1a, (d) classified image obtained by applying “proposed classification method” on Figure 1a, (e) IKONOS image showing fallow, water bodies, vegetation and built-up area categories, (f) classified image obtained by applying “HE-VAR and PAN” based method on Figure 1e, (g) classified images obtained by applying “MCLBP and VAR” based method on Figure 1e, (h) Classified images obtained by applying “proposed classification method” on Figure 1e.
The segmented image is subsequently used to obtain the textured and non-textured region from the initial image P. This process’s mathematical representation is shown as follows:
where P, Γ, R1 and R2 indicates original image, segmented image, extracted non-textured region from original image P and extracted textured region from original image P respectively.
Initially, a threshold is used to segment the transformed image into textured and non-textured region. Afterward, the original image is extracted into textured and non-textured regions using the segmented image mask and clustered independently. The extracted textured region (R2) is clustered by means of ISODATA clustering algorithm [28] considering HE, VAR and intensity values of individual pixel of textured area. The clustering algorithm of ISODATA is less computational, easy and non-supervisory. Whereas the non-textured area (R1) of the image is categorized using the clustering algorithm of ISODATA. In the event of non-textured region, the individual pixel HE and VAR value is not regarded for classification as there is no important variation in texture between classes. The classified outputs of the non-textured and textured region are subsequently produced separately and mixed together to obtain the final classified image.
This research uses “HE-VAR and PAN” and “MCLBP and VAR” based clustering technique to show the power of the suggested clustering technique. The technique based on “HE-VAR and PAN” clusters the entire image using the HE, VAR and intensity of each pixel of the IKONOS PAN image. The suggested technique of clustering is then contrasted with the outcomes of the clustering method based on “HE-VAR and PAN” and “MCLBP and VAR” to demonstrate the strength of the suggested technique of clustering.
The projected clustering method imagines threshold δ to get the segmented image mask from the transformed image. The threshold is computed using a constant ‘K’. In this study, proposed clustering procedure is implemented on IKONOS PAN image with spatial resolution 1 m for ‘K’ values between 3 and 7 and subsequently, classification rate is measured for these ‘K’ values using the ground truth data. The classification accuracy with different ‘K’ is shown in Figure 2. The ‘K’ affects the accuracy in classifying High spatial resolution images considerably as shown in Figure 2. For computing texture, a suitable choice of ‘K’ is important. In this study, superlative performance in high-resolution image classification was accomplished with K = 5. The optimum K is discovered based on Figure 1a and is also implemented in the classification of Figure 1e in addition to other images and found classification accuracy is more than 88%. Thus, from the present study, we can infer that the same K value is suitable for most images.
Classification accuracy as a function of K.
The Proposed clustering method, “MCLBP and VAR” based method and “HE-VAR and PAN” based method were applied on two different 1 m PAN (IKONOS) images (size 256 × 256 pixels) covering (i) vegetation, (ii) built-up area, (iii) water bodies, and (iv) fallow (shown in Figure 1a,e). Texture is observable in in Figure 1a,e. The results of proposed method are then compared with the results obtained from the analysis based on “HE-VAR and PAN” and “MCLBP and VAR” respectively.
Figure 1f–h shows the classification outcomes of the methods “HE-VAR and PAN,” “MCLBP and VAR” and “Proposed classification” after proceeding to the second IKONOS image respectively. Figure 1b–d shows the classification outcomes of the methods “HE-VAR and PAN,” “MCLBP and VAR” and “Proposed classification” after proceeding to the first IKONOS image respectively. Classified images recognize varied features in Figure 1b–d,f–h. From the results, it is evident that the method based on “MCLBP and VAR” gives less heterogeneous segments than the method based on “HE-VAR and PAN,” while the method based on “Proposed classification method” provides more homogeneous segments with distinct classes than the method based on “MCLBP and VAR.”
The ground truth data is collected using GPS equipment for the class vegetation, built-up area, fallow and water body of sample size of 656, 519, 577 and 462 square meters respectively. Afterward, ArcGIS software is used to transfer the ground truth data into vector data. Subsequently, by overlaying the ground truth information distinctly on the results acquired from both IKONOS images (Figure 1a,e) adopting methods such as “HE-VAR and PAN,” “MCLBP and VAR” and “Proposed clustering,” the classification accuracies for each strategy are shown by confusion matrix. The confusion matrices (Table 1) calculated for Figure 1b–d showed that the precision of classification of vegetation, built-up area, fallow and water bodies is (73, 69, 59 and 87% respectively) based on the ‘HE-VAR and PAN’ technique and (79, 71, 68 and 89% respectively) based on the ‘MCLBP and VAR ‘technique, whereas (91, 86, 85 and 94% respectively) by the “Proposed clustering” method. Table 2 demonstrates the confusion matrices calculated for Figure 1f–h showed that the precision of classification of vegetation, built-up area, fallow and water bodies is (73, 74, 66 and 88% respectively) based on the ‘HE-VAR and PAN ‘technique and (78, 76, 68 and 89% respectively) based on the ‘MCLBP and VAR ‘technique whereas (90, 87, 86 and 93% respectively) by the “Proposed clustering” method.
Classes derived from satellites | |||||||||
---|---|---|---|---|---|---|---|---|---|
Classification method | Grand observed class | Vegetation | Built-up area | Fallow | Water body | Row total | Classification accuracy (%) | C (%) | O (%) |
HE-VAR and PAN | Vegetation | 251 | 35 | 12 | 7 | 305 | 72.75 | 27.24 | 15.65 |
Built-up area | 18 | 163 | 14 | 5 | 200 | 69.07 | 30.93 | 15.68 | |
Fallow | 53 | 23 | 144 | 14 | 234 | 59.26 | 40.74 | 37.04 | |
Water body | 23 | 15 | 73 | 177 | 288 | 87.19 | 12.81 | 54.67 | |
Column total | 345 | 236 | 243 | 203 | 1027 | ||||
MCLBP and VAR | Vegetation | 272 | 31 | 11 | 4 | 332 | 78.84 | 18.26 | 13.33 |
Built-up area | 17 | 168 | 7 | 3 | 205 | 71.25 | 28.81 | 11.41 | |
Fallow | 43 | 21 | 166 | 15 | 223 | 68.33 | 31.69 | 32.51 | |
Water body | 13 | 16 | 59 | 181 | 258 | 89.28 | 10.83 | 43.35 | |
Column total | 345 | 236 | 243 | 203 | 1027 | ||||
Proposed method | Vegetation | 313 | 17 | 7 | 3 | 340 | 90.85 | 9.27 | 7.82 |
Built-up area | 7 | 204 | 8 | 2 | 221 | 86.28 | 13.56 | 7.20 | |
Fallow | 19 | 7 | 206 | 7 | 239 | 84.77 | 15.23 | 13.58 | |
Water body | 6 | 8 | 22 | 191 | 227 | 94.11 | 5.91 | 17.73 | |
Column total | 345 | 236 | 243 | 203 | 1027 |
The confusion matrices showing the classification accuracy obtained by applying “HE-VAR and PAN”, “MCLBP and VAR” and “Proposed” methods separately on IKONOS image shown in Figure 1a.
C: Commission error, O: Omission error.
Classes derived from satellites | |||||||||
---|---|---|---|---|---|---|---|---|---|
Classification method | Grand observed class | Vegetation | Built-up area | Fallow | Water body | Row total | Classification accuracy (%) | C (%) | O (%) |
HE-VAR and PAN | Vegetation | 228 | 15 | 43 | 16 | 302 | 73.3 | 26.68 | 23.79 |
Built-up area | 32 | 209 | 14 | 5 | 260 | 73.7 | 26.14 | 18.02 | |
Fallow | 27 | 27 | 221 | 11 | 286 | 66.3 | 33.83 | 19.46 | |
Water body | 24 | 32 | 56 | 227 | 339 | 87.6 | 12.35 | 43.24 | |
Column total | 311 | 283 | 334 | 259 | 1187 | ||||
MCLBP and VAR | Vegetation | 241 | 13 | 38 | 9 | 301 | 77.6 | 22.51 | 20.58 |
Built-up area | 29 | 215 | 13 | 7 | 264 | 75.83 | 24.02 | 17.31 | |
Fallow | 26 | 26 | 228 | 11 | 291 | 68.36 | 31.74 | 18.86 | |
Water body | 15 | 29 | 55 | 232 | 331 | 89.4 | 10.42 | 38.22 | |
Column total | 311 | 283 | 334 | 259 | 1187 | ||||
Proposed method | Vegetation | 281 | 7 | 14 | 6 | 308 | 90.4 | 9.64 | 8.68 |
Built-up area | 3 | 244 | 9 | 5 | 261 | 86.7 | 13.78 | 6.0 | |
Fallow | 15 | 15 | 287 | 7 | 324 | 85.8 | 14.07 | 11.07 | |
Water body | 12 | 17 | 24 | 241 | 294 | 93.2 | 6.94 | 20.46 | |
Column total | 311 | 283 | 334 | 259 | 1187 |
The confusion matrices showing the classification accuracy obtained by applying “HE-VAR and PAN”, “MCLBP and VAR” and “Proposed” methods separately on IKONOS image shown in Figure 1e.
C: Commission error, O: Omission error.
The categorized result for Figure 1a,e shows that the “HE-VAR and PAN” method under segment as a result (i) fallow assorted with water bodies shown in Figure 1b,f, (ii) built-up region assorted with fallow and vegetation shown in Figure 1f, (iii) vegetation assorted with water bodies shown in Figure 1b,f, (iv) fallow assorted with built-up region shown in Figure 1b. This incoherence decreases vegetation, fallow, water bodies and built-up area classification precision as shown in Tables 1 and 2. The technique based on “MCLBP and VAR” somehow overcomes these inconsistencies. It is discovered that, as shown in Figure 1c,g, the superposition of fallow, water body, vegetation region becomes less. In addition, decreased inconsistencies improve the accuracy of the classification of fallow, water body and vegetation regions (see Tables 1 and 2).
“HE-VAR and PAN” based method classifies water bodies and fallow areas as a single class (Figure 1b,f) since the texture patterns of these two areas does not show much difference in high resolution imageries as shown in Figure 1a,e. “MCLBP and VAR” based technique demonstrates improvement in classifying the fallow areas and water bodies which is observable in Figure 1g. But this method could not extract non-textured region appropriately form Figure 1a since MCLBP is sensitive to noise. Therefore “MCLBP and VAR” based method could not discriminate appropriately fallow areas and water bodies in Figure 1a as visible in Figure 1c. HE is not as much of sensitive to noise therefore the proposed technique partitions the image into textured and non-textured regions noticeably which in turn helps in classifying the fallow and water bodies as shown in Figure 1d.
The proposed clustering method is applied further on a 1 m PAN (IKONOS) image (Figure 3a) of (i) urban woodland, (ii) building, (iii) water bodies, and (iv) fallow to show the robustness and validity of the method in classifying land use area. The method satisfactorily discriminate urban woodland, building, fallow and water bodies as shown in Figure 3b. The algorithm also implemented on two extra 1 m PAN (IKONOS) images: (i) Figure 4a of fallow, vegetation, built-up area and bare land and (ii) Figure 4c of water, vegetation, fallow and built-up area. The findings (Figure 4b,d) show that vegetation, fallow, built-up region, bare soil and water bodies are satisfactorily discriminated against by the algorithm.
(a) IKONOS image showing urban woodland, building, water body and fallow categories, (b) classified image obtained by applying “proposed classification method” on Figure 3a.
(a) IKONOS image showing fallow, built-up area, vegetation and bare soil categories, (b) classified image obtained by applying “proposed classification method” on Figure 4a, (c) IKONOS image showing vegetation, fallow, built-up area and water bodies categories, (d) classified image obtained by applying “proposed classification method” on Figure 4c.
In the present study, the spatial structure of local image texture is computed using HE. The contrast around the pixel is measured using VAR. Afterward, the image is transformed using HE and VAR together for measuring the texture. A threshold δ is used to extract textured and non-textured region from the image. The classification algorithm ISODATA is used to classify the textured region taking into account HE, VAR and intensity values of the textured area’s individual pixels. Whereas ISODATA clustering algorithm classifies the extracted non-textured region of the image. The HE and VAR value of individual pixels is not regarded for classification in the event of non-textured region. From the research outcomes, it is discovered that the suggested technique is helpful to extract earth surface characteristics from complicated remote sensing images that contain both textured and non-textured areas. Moreover, it can be considered as an intuitively appealing and unsupervised clustering algorithm for extracting features from remotely sensed images. As a result, the method is potentially useful to extract earth surface features by clustering high spatial resolution panchromatic images more efficiently.
The author sincerely thanks the Director, NRSC, Hyderabad, India and CGM, RCs, NRSC, Hyderabad, India for their support. The author is also grateful to the former GM, RRSC-East for support.
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