Noise Reduction in Hyperspectral Imagery: Overview and Application

• Main Authors: Behnood Rasti, Paul Scheunders, Pedram Ghamisi, Giorgio Licciardi, Jocelyn Chanussot
• Format: Article
• Language: English
• Published: MDPI AG 2018-03-01
• Series: Remote Sensing
• Subjects: classification; denoising; hyperspectral imaging; hyperspectral remote sensing; image analysis; image processing; inverse problems; low-rank; noise reduction; remote sensing; restoration; sparsity; sparse modeling; spectroscopy
• Online Access: http://www.mdpi.com/2072-4292/10/3/482

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.