Deep learning activities on remote sensed 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 well as in every area; is to be able to perform better performance classification than existing approaches and to perceive the feature inferences on its own. In remote sensing, more studies are made especially on hyperspectral images. The most important reason for this is that it can carry a large number of data features. The large number of data features means that there are a large number of attributes for that image. The most important disadvantage of hyperspectral images is; due to the influence of the environment of the device which is shooting the image, various noises may occur. There may be a variety of information loss on this image. Various algorithms techniques have been developed to prevent these losses, while hyperspectral images have been better classified by deep learning models. Recent advances in deep learning models in technological firms and the creation and development of their own deep learning model are evidence of how intense this interest is in this area. Our aim is to examine recent developments in deep learning activities on remote sensing images in this article; to compare the performances obtained by deep learning model and to give brief information about the methods used in this area. © 2018 IEEE.