Abstract
AbstractThe progression of various scientific domains, owing to the enhancement of computer engineering and related technologies, has created a competitive niche. Inter-domain coalescence of certain scientific areas emboldens and envisages a technologically advanced morrow. Deep learning and remote sensing exemplify one such relationship which has bloomed recently. The advancing intervention of deep learning methods has gracefully accelerated the field of remote sensing by outperforming the conventional approaches, thus enhancing it inside out. This paper presents a systematic review of one of the most dug segments of remote sensing, that is, satellite image classification, using a few chosen convolutional neural network frameworks and related approaches. The dataset focused is multispectral Sentinel-2 images which is an open-access source that requires minimal preprocessing. This study explores different architectural convolutional neural network models and their performances for the classification that can assist in various applications.KeywordsConvolutional neural networksRemote sensingSentinel-2Image classification
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