Study of Aerial Image Classification

Abstract

Abstract: Classifying images captured through remote sensing is necessary for various reasons, including monitoring the environment and assessing agriculture. The progress in thisfield has been greatly enhanced by the integration of advancements in machine learning, especially deep learning, and also accessibility of high-definition images obtained through remote sensing. CNNs are now necessary instruments for precise recognition and classificationof objects in aerial imaging. These models use convolution and pooling operations to capture local patterns and relationships, enhancing image classification accuracy. This review paper presents a comprehensive look at the most recent techniques for categorising remote sensing images. It emphasises the significance of accurate classification in the constantly evolving contemporary society and investigates new progress made possible by deep learning methods.The article explores problems in pinpointing unique and important areas in remote sensing images and illustrates how deep learning models tackle these problems through comprehendingrelationships among objects and other elements in the scene. The article assesses the advantagesand disadvantages of various techniques in relation to accuracy, computational efficiency, andscalability through a comparison between traditional methods and deep learning-based methods.