A Comprehensive Review on Deep Learning Model for Multi Class Satellite Image Classification and Prediction

Abstract

Abstract: Satellite images provide a wealth of information about the Earth's surface, but extracting meaningful insights from these vast datasets requires sophisticated analysis techniques. Deep learning models have emerged as powerful tools for multiclass satellite image classification and prediction, offering superior accuracy and efficiency compared to traditional methods. This comprehensive review delves into the latest advancements in deep learning architectures specifically designed for satellite image processing. It critically examines the performance of various convolutional neural network (CNN) architectures, exploring their strengths and limitations in handling diverse satellite image datasets. The review further investigates the integration of transfer learning and domain adaptation techniques to address challenges like limited training data and domain shift. Additionally, it sheds light on recent developments in interpretability and explain ability of deep learning models for satellite image analysis, enabling users to gain deeper understanding of the decision-making process behind predictions. By providing a holistic overview of the current landscape and future directions, this review serves as a valuable resource for researchers and practitioners working in the field of satellite image analysis and deep learning.