An Analysis: Progress in Identifying Objects, Classification and Segmenting them in Satellite Images

Abstract

Abstract: Recognizing and differentiating items in satellite pictures is crucial for different goals, such as city planning and disaster response. Recent developments in machine learning, particularly deep learning, and the abundance of high-quality satellite images have significantly boosted progress in this area. CNNs have become essential for precisely identifying and categorising elements depicted in satellite images. These models use convolution and pooling methods on a local scale to identify local patterns and far-reaching connections, ultimately enhancing object detection and segmentation accuracy. This review offers a detailed summary of the advanced methods used to identify and separate objects in satellite images. The article discusses the importance of precise object detection and segmentation in today's dynamic environment, showcasing the progress achieved through deep learning methodologies. The article explores the difficulties of pinpointing key and distinct areas in satellite pictures and shows how deep learning models tackle this issue by detecting the connections between objects and various facets of the surroundings.