MACHINE LEARNING UNET-BASED SEGMENTATION OF SENTINEL-1A SATELLITE IMAGES

Image segmentation is an area of study which has generated a lot of interest in engineering in the last few years. Segmentation of Sentinel 1A satellite images is an area that is not as common as that of its optical counterparts as a result of the appearance of its images. The focus of this paper is the segmentation of Sentinel-1A Synthetic Aperture Radar (SAR) satellite images using UNet, a Deep learning technique designed specifically for semantic segmentation of images. The model was developed with Python programming language in keras, which is a deep learning Python API running on the Tensorflow framework. The satellite images were acquired from the European Space Agency’s (ESA) Copernicus satellite acquisition hub. The performance evaluation was carried out with Jaccard Index or Intesection over Union (IoU) and Accuracy metrics. The IoU metric measures the number of pixels common between the target and prediction masks divided by the total number of pixels present across both masks while the Accuracy metric reports the percent of pixels in the image which were correctly classified. The results recorded in the evaluation had IoU as 0.42 and an Accuracy of 95%. This show a high performance of the model in segmenting images