Analysis and Processing of Spatial Remote Sensing Multispectral Imagery using Deep Learning Techniques

Abstract

The use of machine learning models, particularly deep learning models, for the analysis of remote sensing products, especially multispectral satellite images, has recently experienced exponential development. Therefore, this article will present a protocol for processing multispectral satellite images by deep learning through the latest methods used in neural networks for computer vision, exploring all the methods used and proposed. In this study, we present the main methods of deep learning adapted to the processing of multispectral satellite images in the form of an efficient processing protocol. Our methodology proceeds with a systematic analysis of all the deep learning concepts by testing the applicability of multispectral satellite images and the contribution of the concept to the accuracy and performance of the model. In addition, each method introduced in this study has been tested in a real use case of remote sensing products especially satellite imagery for spatial analysis tasks such as semantic segmentation, object and pixel classification, object detection, image fusion, and land use and land cover classification (LULC). Thus, a discussion of the use of this protocol and some open challenges in this technological field are presented.