Analysis of Hyperspectral Image Denoising Using Deep Neural Network (DNN) Models

Abstract

Image denoising is considered a common preprocessing step in the analysis and interpretation of hyperspectral images. Nevertheless, most of the methods developed and used previously was adopted for HSI denoising exploit architectures originally developed for grayscale and RGB images which limit the processing of high-dimensional HSI data cubes. As rich spectral information is present in HSI which is to be fully exploited considering the high degree of spectral correlation between adjacent bands in HSIs which gives in resulting poor image denoising, HSI denoising is the most important preprocessing step before the image is being classified. End to end mapping is needed between the clean and noisy images for the dataset by the deep learning method. Conventional low-rank methods lack flexibility for considering the correlation between different HSI which results to loss of information. This paper gives a brief review and analysis of the state-of-the-art available methods for hyperspectral image de-noising with the major advancements, benefits and obstacles in denoising an HSI. Due to limited availability of real time dataset of HSI and equipment expenses, researchers rely on the freely available hyperspectral datasets. This research proposes Hyperspectral image denoising for efficient classification of objects on the earth surface.