Machine Vision and Advanced Image Processing in Remote Sensing: Proceedings of Concerted Action Maviric (Machine Vision in Remotely Sensed Image Compr

ISBN-13: 9783642642609 / Angielski / Miękka / 2011 / 335 str.

Ioannis Kanellopoulos; Graeme G. Wilkinson; Theo Moons

Opis

Since 1994, the European Commission has undertaken various actions to expand the use of Earth observation (EO) from space in the Union and to stimulate value-added services based on the use of Earth observation satellite data.' By supporting research and technological development activities in this area, DG XII responded to the need to increase the cost-effectiveness of space- derived environmental information. At the same time, it has contributed to a better exploitation of this unique technology, which is a key source of data for environmental monitoring from local to global scale. MAVIRIC is part of the investment made in the context of the Environ- ment and Climate Programme (1994-1998) to strengthen applied techniques, based on a better understanding of the link between the remote sensing signal and the underlying bio- geo-physical processes. Translation of this scientific know-how into practical algorithms or methods is a priority in order to con- vert more quickly, effectively and accurately space signals into geographical information. Now the availability of high spatial resolution satellite data is rapidly evolving and the fusion of data from different sensors including radar sensors is progressing well, the question arises whether existing machine vision approaches could be advantageously used by the remote sensing community. Automatic feature/object extraction from remotely sensed images looks very attractive in terms of processing time, standardisation and implementation of operational processing chains, but it remains highly complex when applied to natural scenes.

Spis treści

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