Abstract
It has been shown that image compression based on principal component analysis (PCA) provides good compression efficiency for hyperspectral images. However, PCA might fail to capture all the discriminant information of hyperspectral images, since features that are important for classification tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyperspectral images with pre-encoding discriminant information. A feature extraction method is first applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones. Both feature images and residual images are compressed and transmitted. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor indicate that the proposed method provides better compression efficiency with improved classification accuracy than conventional compression methods.
| Original language | English |
|---|---|
| Article number | 7063895 |
| Pages (from-to) | 1491-1495 |
| Number of pages | 5 |
| Journal | IEEE Geoscience and Remote Sensing Letters |
| Volume | 12 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2015 Jul 1 |
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All Science Journal Classification (ASJC) codes
- Geotechnical Engineering and Engineering Geology
- Electrical and Electronic Engineering
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Hybrid Compression of Hyperspectral Images Based on PCA With Pre-Encoding Discriminant Information. / Lee, Chul Hee; Youn, Sungwook; Jeong, Taeuk; Lee, Eunjae; Serra-Sagristà, Joan.
In: IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 7, 7063895, 01.07.2015, p. 1491-1495.Research output: Contribution to journal › Article
TY - JOUR
T1 - Hybrid Compression of Hyperspectral Images Based on PCA With Pre-Encoding Discriminant Information
AU - Lee, Chul Hee
AU - Youn, Sungwook
AU - Jeong, Taeuk
AU - Lee, Eunjae
AU - Serra-Sagristà, Joan
PY - 2015/7/1
Y1 - 2015/7/1
N2 - It has been shown that image compression based on principal component analysis (PCA) provides good compression efficiency for hyperspectral images. However, PCA might fail to capture all the discriminant information of hyperspectral images, since features that are important for classification tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyperspectral images with pre-encoding discriminant information. A feature extraction method is first applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones. Both feature images and residual images are compressed and transmitted. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor indicate that the proposed method provides better compression efficiency with improved classification accuracy than conventional compression methods.
AB - It has been shown that image compression based on principal component analysis (PCA) provides good compression efficiency for hyperspectral images. However, PCA might fail to capture all the discriminant information of hyperspectral images, since features that are important for classification tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyperspectral images with pre-encoding discriminant information. A feature extraction method is first applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones. Both feature images and residual images are compressed and transmitted. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor indicate that the proposed method provides better compression efficiency with improved classification accuracy than conventional compression methods.
UR - http://www.scopus.com/inward/record.url?scp=85027950675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027950675&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2015.2409897
DO - 10.1109/LGRS.2015.2409897
M3 - Article
AN - SCOPUS:85027950675
VL - 12
SP - 1491
EP - 1495
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
SN - 1545-598X
IS - 7
M1 - 7063895
ER -