TY - GEN
T1 - Two-stage compression of hyperspectral images with enhanced classification performance
AU - Lee, Chulhee
AU - Youn, Sungwook
AU - Lee, Eunjae
AU - Jeong, Taeuk
AU - Serra-Sagristà, Joan
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
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U2 - 10.1117/12.2225568
DO - 10.1117/12.2225568
M3 - Conference contribution
AN - SCOPUS:84991466991
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Remotely Sensed Data Compression, Communications, and Processing XII
A2 - Lee, Chulhee
A2 - Huang, Bormin
A2 - Chang, Chein-I
PB - SPIE
T2 - Remotely Sensed Data Compression, Communications, and Processing XII
Y2 - 20 April 2016 through 21 April 2016
ER -