DEEP LEARNING STUDIES ON REMOTE HYPERSPECTRAL IMAGES

Abstract

In recent years:deep learning models have been widely used on remote sensing images. Deep learning is held on remote sensing images as wellas in everyarea:is`tote ablerto performletterperformance classification than existing approaches and`to perceivertherfeature inferences on its own:In remote sensing:more studies are made especially on hyperspectralimages. The mosfimportanfreasonlorthis is`thafifcan carryalarge numberofdatarfeatures. The large number of data features means that there are a large number of attributes for that image. The most important disadvantage of hyperspectralimages is:due to the influence ofthe environmenfofthe device which is shooting the image:various noises may occur. There may be a variety of information loss on this image. Various algorithms techniques haverteen developed to prevenftheselosses, while hyperspectralimages haverteen betterclassified`ty deep learning models. Recent advances in deep learning models in technological firms and the creation and development oftheirown deeplearning modelare evidence of how intenserthis interesfis in this area. Ouraim is to examine recent developments in deep learning activities on remote sensing images in this article:to compare the performances obtainedty deeplearning modeland`to givetriefinformation aboulthe methods used in this area.