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|Title:||Classification of Detected Changes From Multitemporal High-Res Xband SAR Images: Intensity and Texture Descriptors From SuperPixels|
|Authors:||Barreto, Thiago L. M.|
Rosa, Rafael A. S.
Moreira, Joao R.
Bins, Leonardo S.
Cappabianco, Fabio Augusto Menocci [UNIFESP]
Almeida, Jurandy [UNIFESP]
multilayer perceptron (MLP)
object correlation images (OCIs)
simple linear iterative clustering (SLIC)
synthetic aperture radar (SAR) images
|Publisher:||Ieee-Inst Electrical Electronics Engineers Inc|
|Citation:||Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. Piscataway, v. 9, n. 12, p. 5436-5448, 2016.|
|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.
|Appears in Collections:||Artigo|
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