A modified U-net-based architecture for segmentation of satellite images on a novel dataset

The remote-sensing-based satellite images have been providing a wealth of information to the scientists for study of environmental changes caused by climate changes or human activities such as destructive cyclones and earthquakes etc. This paper proposes a deep learning-based segmentation model for agriculture images captured from satellites and a novel agriculture-based satellite dataset. The segmentation has been performed on the satellite images into five categories of cultivated land, uncultivated land, residences, water, and forest. The dataset has been created using Sentinel-2 satellite data over the Panipat district in Haryana, India having diversity in crops and land usage. The dataset consists of 16,720 images and their corresponding masks over the years ranging from 2018 to 2020. The proposed model consists of a six-phase encoder-decoder network with a total of 33 convolution layers. The proposed segmentation model has been evaluated on proposed dataset and obtained an efficient metric of 72% IoU score which is better than state-of-the-art models such as U-Net, Link-Net, FPN and DeeplabV3+ score 51%, 46%, 49%, 67% IoU respectively.