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- ItemSomente MetadadadosClassification of Detected Changes From Multitemporal High-Res Xband SAR Images: Intensity and Texture Descriptors From SuperPixels(Ieee-Inst Electrical Electronics Engineers Inc, 2016) Barreto, Thiago L. M.; Rosa, Rafael A. S.; Wimmer, Christian; Moreira, Joao R.; Bins, Leonardo S.; Cappabianco, Fabio Augusto Menocci [UNIFESP]; Almeida, Jurandy [UNIFESP]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