Advancements in remote sensing: Harnessing the power of artificial intelligence for scene image classification

The Remote Sensing Scene Image Classification (RSSIC) procedure is involved in the categorization of the Remote Sensing Images (RSI) into sets of semantic classes depending upon the content and this procedure plays a vital role in extensive range of applications, like environment monitoring, urban planning, vegetation mapping, natural hazards' detection and geospatial object detection. The RSSIC procedure exploits Artificial Intelligence (AI) technology, mostly Machine Learning (ML) techniques, for automatic analysis and categorization of the content, present in these images. The purpose is to recognize and differentiate the land cover classes or features in the scene, namely crops, forests, buildings, water bodies, roads, and other natural and man-made structures. RSSIC, using Deep Learning (DL) techniques, has attracted a considerable attention and accomplished important breakthroughs, thanks to the great feature learning abilities of the Deep Neural Networks (DNNs). In this aspect, the current study presents the White Shark Optimizer with DL-driven RSSIC (WSODL-RSSIC) technique. The presented WSODL-RSSIC technique mainly focuses on detection and classification of the remote sensing images under various class labels. In the WSODL-RSSIC technique, the deep Convolutional Neural Network (CNN)-based ShuffleNet model is used to produce the feature vectors. Moreover, the Deep Multilayer Neural network (DMN) classifiers are utilized for recognition and classification of the remote sensing images. Furthermore, the WSO technique is used to optimally adjust the hyperparameters of the DMN classifier. The presented WSODL-RSSIC method was simulated for validation using the remote-sensing image databases. The experimental outcomes infer that the WSODL-RSSIC model achieved improved results in comparison with the current approaches under different evaluation metrics.