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Supervised fusion approach of local features extracted from SAR images for detecting deforestation changes
- Source :
- IET Image Processing, IET Image Processing, Institution of Engineering and Technology, 2019, 13 (14), pp.2866--2876. ⟨10.1049/iet-ipr.2019.0122⟩, IET Image Processing, 2019, 13 (14), pp.2866--2876. ⟨10.1049/iet-ipr.2019.0122⟩
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- International audience; Deforestation has become a major problem consisting of a continuous regression of forested areas in the world, and for this purpose, an efficient detection of these changes has become more than necessary. In this work, a new method for deforestation change detection is proposed. This approach is based on a supervised fusion of local texture features extracted from SAR images. ALOS PALSAR (Advanced Land Observation Satellite Phased Array type L-band Synthetic Aperture Radar) multi-temporal data have been used in this work. Normalised radar cross-section (NRCS) and polarimetric features extracted from HH and HV polarised data allowed recognising different categories of land covers termed as NRCS classification. Grey-level co-occurrence matrix (GLCM) texture features were extracted by using a different moving window sizes applied on local regions previously obtained by binarisation of the NRCS results. A total of 300 samples of regions and five GLCM characteristics have been used here. The detection of deforestation appears clearly in the resulted images with a very satisfactory precision of the reached regions, and the obtained results of the proposed supervised approach have indeed led to very good detection results of the deforestation change.
- Subjects :
- Synthetic aperture radar
Contextual image classification
Computer science
Feature extraction
Polarimetry
020206 networking & telecommunications
02 engineering and technology
15. Life on land
law.invention
Image texture
law
Radar imaging
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Radar
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Software
Change detection
Remote sensing
Subjects
Details
- Language :
- English
- ISSN :
- 17519659 and 17519667
- Database :
- OpenAIRE
- Journal :
- IET Image Processing, IET Image Processing, Institution of Engineering and Technology, 2019, 13 (14), pp.2866--2876. ⟨10.1049/iet-ipr.2019.0122⟩, IET Image Processing, 2019, 13 (14), pp.2866--2876. ⟨10.1049/iet-ipr.2019.0122⟩
- Accession number :
- edsair.doi.dedup.....76776ebbf59936db7d8c660e91e8a9eb