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.
| Original language | English |
|---|---|
| Title of host publication | Remotely Sensed Data Compression, Communications, and Processing XII |
| Editors | Chulhee Lee, Bormin Huang, Chein-I Chang |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510601154 |
| DOIs | |
| Publication status | Published - 2016 Jan 1 |
| Event | Remotely Sensed Data Compression, Communications, and Processing XII - Baltimore, United States Duration: 2016 Apr 20 → 2016 Apr 21 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 9874 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Other
| Other | Remotely Sensed Data Compression, Communications, and Processing XII |
|---|---|
| Country | United States |
| City | Baltimore |
| Period | 16/4/20 → 16/4/21 |
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
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Two-stage compression of hyperspectral images with enhanced classification performance. / Lee, Chul Hee; Youn, Sungwook; Lee, Eunjae; Jeong, Taeuk; Serra-Sagristà, Joan.
Remotely Sensed Data Compression, Communications, and Processing XII. ed. / Chulhee Lee; Bormin Huang; Chein-I Chang. SPIE, 2016. 98740A (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9874).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Two-stage compression of hyperspectral images with enhanced classification performance
AU - Lee, Chul Hee
AU - Youn, Sungwook
AU - Lee, Eunjae
AU - Jeong, Taeuk
AU - Serra-Sagristà, Joan
PY - 2016/1/1
Y1 - 2016/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=84991466991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991466991&partnerID=8YFLogxK
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
ER -