Machine Vision and Advanced Image Processing in Remote Sensing: Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension) - Rilegato

Rilegato

ISBN 10: 3540655719 ISBN 13: 9783540655718

Casa editrice: Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 1999

Riassunto

This volume describes some of the latest developments in the techniques for the analysis of remotely sensed satellite imagery. Focussing in particular on structural information and image understanding, the book will provide a useful guide to emerging techniques which will be of value in the exploitation of the new generation of very high resolution imagery. A number of contributions draw on recent work in the computer vision field. The book explores the increasing links between computer vision and remote sensing, and includes chapters from authors working in the two fields. It brings together the work of a number of European experts who participated in the events of a Concerted Action supported by the Environment and Climate Programme of the European Commission.

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Contenuti

Foreward Introduction PART I: IMAGE PROCESSING AND COMPUTER VISION METHODS FOR REMOTE SENSING DATA G.G. Wilkinson, Recent Developments in Remote Sensing Technology and the Importance of Computer Vision Analysis Techniques; P. Forte, G. A. Jones, Posing Structural Matching in Remote Sensing as an Optimisation Problem; S. Dellepiane, Detail-Preserving Processing of Remote Sensing Images; A.A. Nielsen, Multi-Channel Remote Sensing Data and Orthogonal Transformations for Change Detection; B. Aiazzi et al., Aspects of Multi-Scale Analysis for Managing Spectral and Temporal Coverages of Space-Borne High-Resolution Images; J.M. Carstensen et al., Structural Inference Using Deformable Models; D.P. Argialas, Terrain Feautre Recognition Through Structural Pattern Recognition, Knowledge-Based Systems, and Geomorphometric Techniques; PART II. HIGH RESOLUTION DATA K. Arnason, J.A.Benediktsson, Environmental Mapping Based on High Resolution Remote Sensing Data; P. Boekaerts et al., Potential Role of Very High Resolution Optical Satellite Image Pre-Processing for Product Extraction; T. Häme et al., Forestry Applications of High Resolution Imagery; C. Kontoes, Image Analysis Techniques for Urban Land Use Classification. The Use of Kernel Based Approaches to Process Very High Resolution Satellite Imagery; PART III: VISUALISATION, 3D AND STEREO J. Mundy, R. Curwen, Automated Change Detection in Remotely Sensed Imagery; T. Moons et al., A 3-Dimensional Multi-View Based Strategy for Remotely Sensed Image Interpretation; R. Bolter, A. Pinz, 3D Exloitation of SAR Images; N. Stolte, Visualizing Remotely Sensed Depth Maps using Voxels; H. Nielsen et al., Three Dimensional Surface Registration of Stereo Images and Models from MR Images; W. Di Carlo, Exploring Multi-Dimensional Remote Sensing Data with a Virtual Reality System; PART IV: IMAGE INTERPRETATION AND CLASSIFICATION M. Datcu et al., Information Mining in Remote Sensing Image Archives; J.-P. Berroir, Fusionof Spatial and Temporal Information for Agricultural Land Use Identification - Preliminary Study for the Vegetation Sector; C. Mahlander, D. Rosenholm, Rule-based Identification of Revision Objects in Satellite Images; W. Schneider, Land Cover Mapping from Optical Satellite Images Employing Subpixel Segmentation and Radiometric Calibration; W. Mees, M. Acheroy, Semi-Automatic Analysis of High-Resolution Satellite Images; C.H.M. van Kemenade et al., Density-Based Unsupervised Classification for Remote Sensing; F. Tintrup et al., Classification of Compressed Multispectral Data; PART V: SEGMENTATION AND FEATURE EXTRACTION M. Pesatesi, I. Kanellopoulos, Detection of Urban Features Using Morphological Based Segmentation and Very High Resolution Remotely Sensed Data; I. Gracia, M. Petrou, Non-Linear Line Detection Filters; E. Costamagna et al., Fuzzy Clustering and Pyramidal Hough Transform for Urban Features Detection in High Resolution SAR Images; P. Radeva et al., Detecting Nets of Linear Structures in Satellite Images; A. Pujol et al., Satellite Image Segmentation Through Rotational Invariant Feature Eigenvector Projection; B. Gorte, Supervised Segmentation by Region Merging

Dettagli dell'edizione

• Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
• Data di pubblicazione: 1999
• Lingua: Inglese
• ISBN 10: 3540655719
• ISBN 13: 9783540655718
• Rilegatura: Copertina rigida
• Numero di pagine: 345
• Redattore: Kanellopoulos Ioannis, Wilkinson Graeme G., Moons Theo
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania