Review on Semantic Segmentation of Satellite Images Using Deep Learning

Image segmentation for applications like scene understanding, medical image analysis, robotic vision, video tracking, improving reality, and image compression is a key subject of image processing and image evaluation. Semantic segmentation is an integral aspect of image comprehension and is essential for image processing tasks. Semantic segmentation is a complex process in computer vision applications. Many techniques have been developed, from self-sufficient cars, human interaction, robotics, medical science, agriculture, and so on, to tackle the issue.In a short period, satellite imagery will provide a lot of large-scale knowledge about the earth's surfaces, saving time. With the growth & development of satellite image sensors, the recorded object resolution was improved with advanced image processing techniques. Improving the performance of deep learning models in a broad range of vision applications, important work has recently been carried out to evaluate approaches for deep learning models in image segmentation.In this paper,a detailed overview provides onImage segmentation and describes its techniques likeregion, edge, feature, threshold, and model-based. Also, provide Semantic Segmentation, Satellite imageries, and Deep learning & its Techniques like-DNN, CNN, RNN, RBM, and so on.CNN is one of the efficient deep learning techniques among all of them that can be usedwith the U-net model in further work.