Machine Vision and Advanced Image Processing in Remote Sensing
Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension)
Published on 6. December 2012
X, 335 pages
978-3-642-60105-7 (ISBN)
Description
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.
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Ioannis Kanellopoulos | Graeme G. Wilkinson | Theo Moons
Machine Vision and Advanced Image Processing in Remote Sensing
Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension)
Book
10/2011
Springer
€106.99
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Ioannis Kanellopoulos | Graeme G. Wilkinson | Theo Moons
Machine Vision and Advanced Image Processing in Remote Sensing
Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension)
Book
05/1999
Springer
€149.99
No shipping information available
Content
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.
