TY - GEN
T1 - Low-complexity hyperspectral image compression using folded PCA and JPEG2000
AU - Mei, Shaohui
AU - Khan, Bakht Muhammad
AU - Zhang, Yifan
AU - Du, Qian
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - Hyperspectral image compression by PCA and JPEG2000 can provide excellent rate distortion performance while preserving essential information for a successive application, e.g., classification tasks. However, for onboard applications, PCA suffers from high computational complexity and large memory requirements due to the eigen-analysis of highdimensional covariance matrix. Therefore, a computationally more efficient analysis, namely Folded Principal Component Analysis (FPCA) is adopted to perform dimension reduction and combined with JPEG2000 for compression. In FPCA, the spectral vector of hyperspectral pixels is folded into a matrix to compute covariance matrix, by which the dimension of covariance matrix is highly reduced. As a result, both computational complexity and memory requirement in subsequent eigen-analysis is reduced. Experimental results demonstrate that the proposed FPCA+JPEG2000 based compression scheme outperforms existing PCA+JPEG2000 in terms of rate distortion and classification after de-compression.
AB - Hyperspectral image compression by PCA and JPEG2000 can provide excellent rate distortion performance while preserving essential information for a successive application, e.g., classification tasks. However, for onboard applications, PCA suffers from high computational complexity and large memory requirements due to the eigen-analysis of highdimensional covariance matrix. Therefore, a computationally more efficient analysis, namely Folded Principal Component Analysis (FPCA) is adopted to perform dimension reduction and combined with JPEG2000 for compression. In FPCA, the spectral vector of hyperspectral pixels is folded into a matrix to compute covariance matrix, by which the dimension of covariance matrix is highly reduced. As a result, both computational complexity and memory requirement in subsequent eigen-analysis is reduced. Experimental results demonstrate that the proposed FPCA+JPEG2000 based compression scheme outperforms existing PCA+JPEG2000 in terms of rate distortion and classification after de-compression.
KW - Compression
KW - Folded Principal Component Analysis (FPCA)
KW - Hyperspectral Images (HSI)
KW - Principal Component Analysis (PCA)
UR - http://www.scopus.com/inward/record.url?scp=85064190853&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8519455
DO - 10.1109/IGARSS.2018.8519455
M3 - 会议稿件
AN - SCOPUS:85064190853
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4756
EP - 4759
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -