Paper
19 May 2016 Two-stage compression of hyperspectral images with enhanced classification performance
Chulhee Lee, Sungwook Youn, Eunjae Lee, Taeuk Jeong, Joan Serra-Sagristà
Author Affiliations +
Abstract
Most compression methods for hyperspectral images have been optimized to minimize mean squared errors. However, this kind of compression method may not retain all discriminant information, which is important if hyperspectral images are to be used to distinguish among classes. In this paper, we propose a two-stage compression method for hyperspectral images with encoding residual discriminant information. In the proposed method, we first apply a compression method to hyperspectral images, producing compressed image data. From the compressed image data, we produce reconstructed images. Then we generate residual images by subtracting the reconstructed images from the original images. We also apply a feature extraction method to the original images, which produces a set of feature vectors. By applying these feature vectors to the residual images, we generate discriminant feature images which provide the discriminant information missed by the compression method. In the proposed method, these discriminant feature images are also encoded. Experiments with AVIRIS data show that the proposed method provides better compression efficiency and improved classification accuracy than other compression methods.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chulhee Lee, Sungwook Youn, Eunjae Lee, Taeuk Jeong, and Joan Serra-Sagristà "Two-stage compression of hyperspectral images with enhanced classification performance", Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740A (19 May 2016); https://doi.org/10.1117/12.2225568
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image compression

Hyperspectral imaging

Hyperspectral imaging

Principal component analysis

Image classification

Image classification

Signal to noise ratio

Back to Top