Noise Reduction in Hyperspectral Imagery: Overview and Application

Remote Sensing

Paul Scheunders¹  Giorgio Licciardi²  Jocelyn Chanussot³  Behnood Rasti⁴  Pedram Ghamisi⁵ 

[1] The Department of Electrical and Computer Engineering, University of Iceland, Sæmundargata 2, 101 Reykjavik, Iceland;German Aerospace Center (DLR), Earth Observation Center, Remote Sensing Technology Institute, SAR Signal Processing, Oberpfaffenhofen, 82234 Wessling, Germany;Hypatia Research Consortium, 00133 Roma, Italy;Keilir Institute of Technology (KIT), Grænásbraut 910, 235 Reykjanesbær, Iceland;Visionlab, University of Antwerp (CDE) Universiteitsplein 1 (N Building), B-2610 Antwerp, Belgium;

关键词: classification;    denoising;    hyperspectral imaging;    hyperspectral remote sensing;    image analysis;    image processing;    inverse problems;    low-rank;    noise reduction;    remote sensing;    restoration;    sparsity;    sparse modeling;    spectroscopy;   

DOI  :  10.3390/rs10030482

来源: DOAJ

【 摘 要 】

Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth’s surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) noises which include thermal (Johnson) noise, quantization noise, and shot (photon) noise. Noise reduction is often considered as a preprocessing step for hyperspectral imagery. In the past decade, hyperspectral noise reduction techniques have evolved substantially from two dimensional bandwise techniques to three dimensional ones, and varieties of low-rank methods have been forwarded to improve the signal to noise ratio of the observed data. Despite all the developments and advances, there is a lack of a comprehensive overview of these techniques and their impact on hyperspectral imagery applications. In this paper, we address the following two main issues; (1) Providing an overview of the techniques developed in the past decade for hyperspectral image noise reduction; (2) Discussing the performance of these techniques by applying them as a preprocessing step to improve a hyperspectral image analysis task, i.e., classification. Additionally, this paper discusses about the hyperspectral image modeling and denoising challenges. Furthermore, different noise types that exist in hyperspectral images have been described. The denoising experiments have confirmed the advantages of the use of low-rank denoising techniques compared to the other denoising techniques in terms of signal to noise ratio and spectral angle distance. In the classification experiments, classification accuracies have improved when denoising techniques have been applied as a preprocessing step.

【 授权许可】

Unknown