Jie Chen

Northwestern Polytechnical University China

He is currently a professor at the Center of Intelligent Acoustics and Immersive Communications, School of Marine Science and Technology, Northwestern Polytechnical University. He has conducted research at the University of Nice Sophia-Antipolis, France, and the University of Michigan, Ann Abor, USA, from 2009 to 2014. His research interests include distributed optimization, stochastic signal processing, machine learning with applications to acoustic signal processing, and image processing. Dr. Chen served as Technical Program Co-Chair of the International Workshop on Acoustic Echo and Noise Control (2016), Distinguished Lecturer of the Asia-Pacific Signal and Information Processing Association (2018–2019), and Program Co-Chair of the Intelligent Signal and Information Processing Summer School (2019) of the IEEE Signal Processing Society.

1books edited

2chapters authored

Latest work with IntechOpen by Jie Chen

Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods.

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Processing and Analysis of Hyperspectral Data

Edited by Jie Chen

Chapters authored

Sequential Classification of Hyperspectral Images

By Min Zhao and Jie Chen

Hyperspectral imaging has become increasingly popular in applications such as agriculture, food, and environment. Rich spectral information of hyperspectral images leads to new possibilities and new challenges in data processing. In this chapter, we consider the hyperspectral classification problems in consideration of sequential data collection, which is a frequent setting in industrial pushboom imaging systems. We present related techniques including data normalization, dimension reduction, classification, and spatial information integration and the way to accommodate these techniques to the context of sequential data collecting and processing. The propose scheme is validated with real data collected in our laboratory. The methodology of result assessment is also presented.

Part of the book: Hyperspectral Imaging in Agriculture, Food and Environment

Graph Construction for Hyperspectral Data Unmixing

By Zeng Li, Jie Chen and Susanto Rahardja

This chapter presents graph construction for hyperspectral data and associated unmixing methods based on graph regularization. Graph is a ubiquitous mathematical tool for modeling relations between objects under study. In the context of hyperspectral image analysis, constructing graphs can be useful to relate pixels in order to perform corporative analysis instead of analyzing each pixel individually. In this chapter, we review fundamental elements of graphs and present different ways to construct graphs in both spatial and spectral senses for hyperspectral images. By incorporating a graph regularization, we then formulate a general hyperspectral unmixing problem that can be important for applications such as remote sensing and environment monitoring. Alternating direction method of multipliers (ADMM) is also presented as a generic tool for solving the formulated unmixing problems. Experiments validate the proposed scheme with both synthetic data and real remote sensing data.

Part of the book: Hyperspectral Imaging in Agriculture, Food and Environment

Jie Chen

Latest work with IntechOpen by Jie Chen

Processing and Analysis of Hyperspectral Data

Chapters authored

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