Part of the book: Numerical Analysis
Part of the book: Computational and Numerical Simulations
In processing of multichannel remote sensing data, there is a need in automation of basic operations as filtering and compression. Automation presumes undertaking a decision on expedience of image filtering. Automation also deals with obtaining of information based on which certain decisions can be undertaken or parameters of processing algorithms can be chosen. For the considered operations of denoising and lossy compression, it is shown that their basic performance characteristics can be quite easily predicted based on easily calculated local statistics in discrete cosine transform (DCT) domain. The described methodology of prediction is shown to be general and applicable to different types of noise under condition that its basic characteristics are known in advance or pre-estimated accurately.
Part of the book: Environmental Applications of Remote Sensing
In this chapter, we consider lossy compression of multichannel images acquired by remote sensing systems. Two main features of such data are taken into account. First, images contain inherent noise that can be of different intensity and type. Second, there can be essential correlation between component images. These features can be exploited in 3D compression that is demonstrated to be more efficient than component-wise compression. The benefits are in considerably higher compression ratio attained for the same or even less distortions introduced. It is shown that important performance parameters of lossy compression can be rather easily and accurately predicted.
Part of the book: Recent Advances in Image and Video Coding
This chapter addresses an important practical task of classification of multichannel remote sensing data with application to multitemporal dual-polarization Sentinel radar images acquired for agricultural regions in Ukraine. We first consider characteristics of dual-polarization Sentinel radar images and discuss what kind of filters can be applied to such data. Several examples of denoising are presented with analysis of what properties of filters are desired and what can be provided in practice. It is also demonstrated that the use of preliminary denoising produces improvement of classification accuracy where despeckling that is more efficient in terms of standard filtering criteria results in better classification.
Part of the book: Recent Advances and Applications in Remote Sensing