Land Use Land Cover Segmentation using Synthetic Aperture Radar Data on Dhaka Division, Bangladesh

Abstract

Bangladesh is an agrarian economy where agriculture contributes 19.3% of the gross domestic product (GDP) of the country. [1] Rice is the dominant crop and the staple food that meets the nutritional demand of the large population of the country. It is becoming increasingly important to monitor paddy production in the country and detect threats to the crop production due to climate change, natural disasters, urbanization and other factors in order to utilize governmental aid and resources as efficiently as possible as well as take preventive measures. Clouds cover the sky of Bangladesh from the months of May through October, which overlaps with the major rice crop cultivation season of Aus and Boro. Optical images with optical remote sensing simply fail to capture the condition of the crops during these cloudy months. This research proposes the use of Synthetic Aperture Radar (SAR) images to penetrate through clouds during the cloudy months in Bangladesh. We conducted our research over the area of Dhaka district using Sentinel-1A images collected from Copernicus Open Access Hub website and an annotated Bing image as ground truth with five major areas of interest to create the training and testing datasets to input to a UNET model, which is a popular deep learning architecture for image segmentation tasks. We find out that despite the unfavorable weather and presence of clouds, our model performs really well with a 76% accuracy and promising results to support the usage of Sentinel-1A images to monitor areas during monsoon season.