Supervised fusion approach of local features extracted from SAR images for detecting deforestation changes. Issue 14 (23rd October 2019)

• Record Type: Journal Article
• Authors: Horch, Abdelkader; Djemal, Khalifa; Gafour, Abdelkader; Taleb, Nasreddine
• Is Part Of: IET image processing. Volume 13:Issue 14(2019)
• Page Start: 2866
• Page End: 2876
• Publication Date: 2019-10-23
• Subjects: radar imaging -- radar cross‐sections -- synthetic aperture radar -- feature extraction -- image classification -- radar polarimetry -- geophysical image processing -- image texture -- remote sensing by radar; supervised fusion approach -- local features -- SAR images -- continuous regression -- forested areas -- deforestation change detection -- local texture features -- ALOS PALSAR -- Advanced Land Observation Satellite -- Array type L‐band Synthetic Aperture Radar -- multitemporal data -- polarimetric features -- NRCS classification -- grey‐level co‐occurrence matrix texture features -- different moving window -- local regions -- NRCS results -- resulted images -- supervised approach -- good detection results; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0122
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.