Machine Vision and Advanced Image Processing in Remote Sensing -

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Sommaire:
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.