Deforestation change detection using high-resolution multi-temporal X-Band SAR images and supervised learning classification

Remote sensing has been widely applied for environmental monitoring by means of change detection techniques, commonly for identifying deforestation signs which is the gateway for illegal activities such as uncontrolled urban growth and grazing pasture. Monthly acquired X-Band images from airborne Synthetic Aperture Radar (SAR) provided multi-temporal scenes employed in this work resulting in environmental incident reports forwarded to the responsible authorities. The present work proposes the use of both, Superpixel segmentation by Simple Linear Iterative Clustering (SLIC) and change detection by Object Correlation Images (OCI) not yet applied to multi-temporal X-Band high resolution SAR images, and the application of a simple Multilayer Perceptron (MLP) supervised learning technique for detecting and classifying the changes into relevant activities. Experiments have been performed using acquired SAR imagery from BRADAR airborne sensor OrbiSAR-2 under Brazilian Atlantic Forest which revealed possible deforestation activities comparing achieved results with those obtained with experts.