The insufficient land cover data contain mainly imperfect the consequence and effects of land cover. Although satellite imaging or remote sensing is used in mapping various spatial and temporal scales, however, its complete endeavor was not hitherto recognized. Therefore, this paper aims to employ a new land cover classification technique by optimal deep learning architecture. Moreover, it comprises three major stages such as segmentation, feature classification, and extraction. At first, the land cover image is segmented and given to the feature extraction process. For feature extraction, VI, like SR, Kauth–Thomas Tasseled Cap and NDVI, are extracted. Moreover, these features are classified by exploiting CNN and NN in both the classifiers, by Enhanced Crow Search Algorithm the number of hidden neurons is optimized. The optimization of hidden neurons is performed so that the classification accuracy must be maximum that is considered as the main contribution.
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