Chen C.H. (ed.). Frontiers of Remote Sensing Information Processing

World Scientific, 2003. — 627 p. — ISBN10: 9812383441, ISBN13: 978-9812383440.The remote sensing area has experienced rapid growth in recent years. One major factor that impacts the growth is the numerous information processing techniques originated from the significant progress in other fields such as image and signal processing, pattern recognition, artificial intelligence, computer vision, and related areas such as neural networks, fuzzy logic, etc. There has also been a number of new mathematical approaches for example in wavelet transform, deconvolution, etc., which motivated development of more efficient information processing techniques for use in remote sensing.
The book presents chapters that highlight some frontier work in remote sensing information processing. . I am very honored to have leaders in the field to prepare the chapters that present readers with these frontier developments Although no attempt is made to cover every topic, the representative chapters in each topic should give readers a good insight of the status of remote sensing information processing. Topics are not arranged according to their order of importance. In fact all topics covered are of equal importance. Chapter lengths may vary, but they are equally significant.Remote Sensing Sensors: Capabilities and Information Processing Requirements
Transform Methods in Remote Sensing Information Processing
Data Mining
Scene Modeling and Image Mining With A Visual Grammar
A Shape-Based Approach to Change Detection and Information Mining in Remote Sensing
SAR Image Processing
A Review of Polarization Orientation Angle Estimation from Polarimetric Sar Data
Unsupervised Classification of Natural Scenes from Polarimetric Interferometric Sar Data
Wavelet Analysis and Applications
Wavelet Analysis of Satellite Images in Ocean Applications
Wavelet-Based SAR Speckle Filters
A Wavelet Representation of Multispectral Images
Military Applications of Remote Sensing
Automating The Estimation of Various Meteorological Parameters Using Satellite Data and Machine Learning Techniques
Microwave Remote Sensing
Reconstruction and Resolution Enhancement Techniques for Microwave Sensors
Statistical Pattern Recognition
Advanced Classification Techniques: Partially Supervised Approaches
Nearest Neighbor Decision Rule for Pixel Classification in Remote Sensing
Unsupervised Segmentation of Hyperspectral Images Using Gauss-Markov Random Fields and PCA
Automatic Target Segmentation
Multisensor Automatic Target Segmentation
Neural Networks
Category Classification Using Neural Networks
Application of Multiresolution Remote Sensing Image Analysis and Neural Techniques to Power Lines Surveillance
Change Detection
Advances in Unsupervised Change Detection
Seismic Signal Processing
Remote Detection Using Dual Sensors
Edge-Preserving Smoothing for Enhancing 3-D Seismic Images
Imaging Seismic Reflection Data at The Correct Depth Without Specifying An Accurate Velocity Model: Initial Numerical Examples of An Inverse Scattering Subseries
Time Series Prediction
Seasonality Extraction from Time-Series of Satellite Sensor Data
Image Compression
Near-Lossless Compression of Remote Sensing Data
Emerging Topics
Image Enhancement in Ground Penetrating Radar Signal Processing to Detect Landmines
Infra-Red Image Processing
Hyperspectral Imaging Analysis and Applications