This work describes some of the late-1990s developments in the techniques for the analysis of remotely sensed satellite imagery. It explores the increasing links between computer vision and remote sensing, and includes chapters from authors working in the two fields.

Foreword.- I. Image Processing and Computer Vision Methods for Remote Sensing Data.- Recent Developments in Remote Sensing Technology and the Importance of Computer Vision Analysis Techniques.- Posing Structural Matching in Remote Sensing as an Optimisation Problem.- Detail-Preserving Processing of Remote Sensing Images.- Multi-Channel Remote Sensing Data and Orthogonal Transformations for Change Detection.- Aspects of Multi-Scale Analysis for Managing Spectral and Temporal Coverages of Space-Borne High-Resolution Images.- Structural Inference Using Deformable Models.- Terrain Feature Recognition Through Structural Pattern Recognition, Knowledge-Based Systems, and Geomorphometric Techniques.- II. High Resolution Data.- Environmental Mapping Based on High Resolution Remote Sensing Data.- Potential Role of Very High Resolution Optical Satellite Image Pre-Processing for Product Extraction.- Forestry Applications of High Resolution Imagery.- Image Analysis Techniques for Urban Land Use Classification. The Use of Kernel Based Approaches to Process Very High Resolution Satellite Imagery.- III. Visualisation, 3D and Stereo.- Automated Change Detection in Remotely Sensed Imagery.- A 3-Dimensional Multi-View Based Strategy for Remotely Sensed Image Interpretation.- 3D Exploitation of SAR Images.- Visualizing Remotely Sensed Depth Maps using Voxels.- Three Dimensional Surface Registration of Stereo Images and Models from MR Images.- Exploring Multi-Dimensional Remote Sensing Data with a Virtual Reality System.- IV. Image Interpretation and Classification.- Information Mining in Remote Sensing Image Archives.- Fusion of Spatial and Temporal Information for Agricultural Land Use Identification - Preliminary Study for the VEGETATION Sensor.- Rule-based Identification of Revision Objects in Satellite Images.- Land Cover Mapping from Optical Satellite Images Employing Subpixel Segmentation and Radiometric Calibration.- Semi-Automatic Analysis of High-Resolution Satellite Images.- Density-Based Unsupervised Classification for Remote Sensing.- Classification of Compressed Multispectral Data.- V. Segmentation and Feature Extraction.- Detection of Urban Features Using Morphological Based Segmentation and Very High Resolution Remotely Sensed Data.- Non-Linear Line Detection Filters.- Fuzzy Clustering and Pyramidal Hough Transform for Urban Features Detection in High Resolution SAR Images.- Detecting Nets of Linear Structures in Satellite Images.- Satellite Image Segmentation Through Rotational Invariant Feature Eigenvector Projection.- Supervised Segmentation by Region Merging.

Good overview of the state of the art in Europe in advanced image analysis techniques for remote sensing Insight to computer vision techniques and software tools that could be used in future remote sensing projects Focusses primarily on new (and forthcoming) very high-resolution satellite imagery