APPLICATION OF DEEP LEARNING TECHNIQUES TO DETECT DEFORESTATION USING SAR DATA

Deforestation is one of the major natural issues that are influencing this present reality. To screen and identify deforestation, different remote detecting procedures have been created throughout the long term. In any case, the discovery of deforestation utilizing Engineered Gap Radar (SAR) information has been an area of dynamic examination lately. SAR information enjoys the benefit of having the option to enter mists and is accordingly more valuable in regions with weighty overcast cover. In this paper, we present a profound learning-based approach for the location of deforestation utilizing SAR information. Our methodology depends on Convolutional Brain Organizations (CNNs) and involves SAR information as info. We show that our methodology beats customary AI procedures for deforestation discovery.