Classification of Detected Changes From Multitemporal High-Res Xband SAR Images: Intensity and Texture Descriptors From SuperPixels

Classification of Detected Changes From Multitemporal High-Res Xband SAR Images: Intensity and Texture Descriptors From SuperPixels

Author Barreto, Thiago L. M. Google Scholar
Rosa, Rafael A. S. Google Scholar
Wimmer, Christian Google Scholar
Moreira, Joao R. Google Scholar
Bins, Leonardo S. Google Scholar
Cappabianco, Fabio Augusto Menocci Autor UNIFESP Google Scholar
Almeida, Jurandy Autor UNIFESP Google Scholar
Abstract Remote sensing has been widely employed for monitoring land cover and usage by change detection techniques. In this paper, we cope with the early detection of the first signs of deforestation, which is the gateway for illegal activities, such as unauthorized urban sprawl and grazing use. In recent years, object-based approaches have emerged as a more suitable alternative than pixel-based methods for change detection in remote sensing images. Even though several classifiers have been tested, there was little effort in selecting appropriated features for the classification of detected changes. After a deep analysis of the existing segmentation, feature extraction, and classification approaches, we propose an object-based methodology that consists of: 1) segmenting multitemporal Xband high-resolution synthetic aperture radar (SAR) images into superpixels employing the simple linear iterative clustering algorithm

2) extracting features using the object correlation images framework and with the gray-level cooccurrence matrix

and 3) classifying areas into unchanged, deforestation, and other changes by means of a multilayer perceptron supervised learning technique. Experiments were performed using high-resolution SAR images obtained by the airborne sensor OrbiSAR-2 from BRADAR in challenging scenarios of the Brazilian Atlantic Forest, including a wide variety of vegetation, rivers, sea coasts, urban, harvest and open areas, and humidity changes. We perform an extensive experimental analysis of the results, comparing the proposed method with a state-of-the-art approach. The results demonstrate that our method yields an improvement of over 10% in the accuracy while detecting changes and classifying deforested areas.
Keywords Change detection
multilayer perceptron (MLP)
object correlation images (OCIs)
remote sensing
simple linear iterative clustering (SLIC)
synthetic aperture radar (SAR) images
xmlui.dri2xhtml.METS-1.0.item-coverage Piscataway
Language English
Sponsor Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Grant number FAPESP: 2016/06441-7
CNPq: 486988/2013-9
Date 2016
Published in Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. Piscataway, v. 9, n. 12, p. 5436-5448, 2016.
ISSN 1939-1404 (Sherpa/Romeo, impact factor)
Publisher Ieee-Inst Electrical Electronics Engineers Inc
Extent 5436-5448
Access rights Closed access
Type Article
Web of Science ID WOS:000391468100015

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