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Contextual Descriptors for Scene Classes in Very High Resolution SAR Images
- Source :
- IEEE Geoscience and Remote Sensing Letters
- Publication Year :
- 2012
-
Abstract
- The new generation of spaceborne SAR instruments with meter or submeter resolution finds enormous applications for the observation of urban, industrial, in general of man-made scenes. Thus, targets are not any more observed in isolation, instead the groups of objects, e.g., house, bridge, and road, etc., need to be recognized in their spatial context. This paper proposes a feature extraction method for image patches in order to capture the spatial context. The method is based on the characteristics of the spectra of the SAR data, integrating radiometric, geometric, and texture properties of the SAR image patch. The method is demonstrated for TerraSAR-X High Resolution Spotlight data. To account for the spatial context in which a group of targets is located, it uses an image patch covering typically 200 × 200 m2 of the scene. A comparative evaluation of our descriptors and grey-level co-occurrence matrix (GLCM) texture features has been performed over a database of 6916 patches. The method allowed for the robust recognition of over 30 different scene classes, with precision between 50% and 93%. Numerical results show that our method is able to discriminate between scene classes better than GLCM texture parameters.
- Subjects :
- Synthetic aperture radar
Computer science
Feature extraction
0211 other engineering and technologies
Context (language use)
02 engineering and technology
Spectral line
Databases
Image texture
Image resolution
Radar imaging
0202 electrical engineering, electronic engineering, information engineering
Computer vision
Electrical and Electronic Engineering
021101 geological & geomatics engineering
Spatial contextual awareness
Pixel
Contextual image classification
business.industry
Context
Data models
020206 networking & telecommunications
Pattern recognition
Remote sensing
Geotechnical Engineering and Engineering Geology
Artificial intelligence
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- IEEE Geoscience and Remote Sensing Letters
- Accession number :
- edsair.doi.dedup.....7d4958396eacbc98b80bd30378b17cd4