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Deforestation detection in Amazon rainforest with multitemporal X-band and p-band sar images using cross-coherences and superpixels

Forest monitoring is a major concern today due to climate changes, conservation of fauna and flora and to the lack of water. Therefore, several environmental monitoring techniques have been developed and used to detect changes in the scenes. The use of SAR (synthetic aperture radar) seems appropriate to detect changes due to its independence of atmospheric and lighting conditions. The SAR change detection is a process that uses SAR images acquired in the same geometric conditions but in different moments (multitemporal) to identify changes in the surface that occurred between two acquisitions. This paper presents a new method of change detection in multitemporal SAR images using X- and P-band SAR images simultaneously to calculate a change detection indicator image (binary mask) based in the coherences between all the images used as attributes calculated from superpixel segments to define a change detection neural network. Experimental tests were conducted using real SAR data obtained by the airborne sensor OrbiSAR-2 from Bradar in the Amazon Forest (Equatorial Rain Forest) and the results showed good quality detections.