Satellite image classification is a significant piece of utilizations in different fields, for example, horticulture, nature observing, and disaster management. This work is expected to improve the spatial and spectral information of satellite images by using higher request bits of knowledge in a blend with power shade l inundation utilizing Adaptive Supervised Multi-Resolution demonstrating classification approach. The proposed Adaptive Supervised Multi-Resolution based strategy that naturally orders the various regions from spatiotransient remote detecting pictures. Initially, a kernel representation has been planned by the structure of multispectral and temporal remote detecting information. Also, the Adaptive Supervised Multi-Resolution system with tweaked parameters has been proposed for preparing region tests and learning spatiotemporal discriminative representations. The following parameters are used to assess the execution of the proposed Adaptive Supervised Multi-Resolution: sensitivity, specificity, accuracy, and false classification ratio. To reduce the dimensionality of multi-band satellite images, the autonomous part assessment is used, which uses the higher-organize experiences of the data independently. The exhibition of the proposed technique was approved through simulation utilizing the MATLAB programming. Compared with ordinary satellite image classifier with a proposed Adaptive Supervised Multi-Resolution strategy based classifier execution by accomplishing the result of 97.695% in accuracy, 94.815% in sensitivity, and 97.75% in specificity.
