|
|
|
|
|
|
A Fast Lossless Data Compression Method for the Wedge Filter Spectral Imager |
LI Hong-bo1, 2, HU Bing-liang1*, YU Lu1, 2, WEI Rui-yi1, YU Tao1 |
1. Laboratory of Spectral Imaging Technique, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
2. University of Chinese Academy of Sciences, Beijing 100049, China |
|
|
Abstract Wedge filter spectral imager, with no moving components and low complexity, has become an important development direction of low cost miniature imaging spectrometer. Based on the state of the art hyperspectral lossless compression standard CCSDS123, we propose a lossless data compression method for the wedge filter spectral imager. The proposed method redefines the local difference vector in CCSDS123, taking fully advantage of the spatial-spectral co-modulation characteristics of the wedge filter spectral imager. To compress the raw data from a wedge filter spectral imager, the compression encoder firstly predicts the sample value using its local sum and local difference vector, then computes a prediction residual and the corresponding mapped prediction residual, finally encodes the mapped prediction residual via a sample-adaptive entropy coding approach. The proposed method can effectively compress the raw data from a wedge filter spectral imager by using the local correlation in the spatial-spectral space. To verify the compression performance of the proposed method, experiments are taken on 6 raw datasets containing different scenes. The results show that the proposed method surpasses the original CCSDS123 method by about 21.62% higher compression ratio on the test datasets with almost the same computational time.
|
Received: 2017-12-13
Accepted: 2018-04-15
|
|
Corresponding Authors:
HU Bing-liang
E-mail: hbl@opt.ac.cn
|
|
[1] Moriya E A S, Imai N N, Tommaselli A M G, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(2): 740.
[2] Kim J H, Kim J, Yang Y, et al. Optical Engineering, 2017, 56(5): 053101.
[3] Toth C, Józków G. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 115: 22.
[4] Dusselaar R, Paul M. Journal of the Optical Society of America A, 2017, 34(12): 2170.
[5] YU Lu, LIU Xue-bin, LI Hong-bo, et al(余 璐, 刘学斌, 李洪波, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(3): 939.
[6] Garcia-Vilchez F, Muoz-Marí J, Zortea M, et al. IEEE Geoscience and Remote Sensing Letters, 2011, 8(2): 253.
[7] Emmanuel Christophe. Optical Remote Sensing-Advances in Signal Processing and Exploitation Techniques, Chapter Hyperspectral Data Compression Tradeoff. Berlin: Springer,2011. 9.
[8] Consultative Committee for Space Data Systems. Lossless Multispectral & Hyperspectral Image Compression CCSDS 123.0-B-1. 2012.
[9] Santos L, Berrojo L, Moreno J, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(2): 757.
[10] Davidson R L, Bridges C P. IEEE Aerospace Conference, Bigsky, MT, 2017. 3.
[11] Liu S, Zhang C, Zhang Y, et al. SPIE AOPC: Optical Spectroscopy and Imaging, 2017, 10461: 104611K.
[12] Adāo T, Peres E, Pádua L, et al. Proceedings of the Small Unmanned Aerial Systems for Environmental Research, Vila Real, Portugal, 2017. 28.
[13] Clark P, Malphrus B, Reuter D, et al. SPIE CubeSats and NanoSats for Remote Sensing, 2017, 9978: 99780C.
[14] Udelhoven T, Schlerf M, Segl K, et al. Sensors, 2017, 17(7): 1542.
[15] Augé E, Sánchez J E, Kiely A, et al. Journal of Applied Remote Sensing, 2013, 7: 074594. |
[1] |
ZHANG Zhou-feng 1, 2, 3, HU Bing-liang1*, YIN Qin-ye2, GAO Xiao-hui1 . Research on Broadband Spectral Imaging Spectrometer Based on CDP [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(07): 2284-2286. |
[2] |
YANG Jin1, 2, CUI Ji-cheng1, Bayanheshig1, QI Xiang-dong1, TANG Yu-guo1*, YAO Xue-feng1. Study on the Design of Prism Hyperspectral Imaging System Based on Off-Axis Two-Mirror Littrow Configuration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(05): 1537-1542. |
[3] |
LI Jin1,2, JIN Long-xu1, LI Guo-ning1. Lossless Compression of Hyperspectral Image for Space-Borne Application[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(08): 2264-2269. |
[4] |
SUN Ling1, 2, GUO Mao-hua3, XU Na1, 2, ZHANG Li-jun1, 2, LIU Jing-jing1, 2, HU Xiu-qing1, 2, LI Yuan1, 2, RONG Zhi-guo1, 2, ZHAO Ze-hui4. On-Orbit Response Variation Analysis of FY-3 MERSI Reflective Solar Bands Based on Dunhuang Site Calibration [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(07): 1869-1877. |
[5] |
GOU Zhi-yang1,2, YAN Lei1,2*, CHEN Wei1,2, JING Xin1,2, YIN Zhong-yi1,2, DUAN Yi-ni1,2 . In-Flight Absolute Radiometric Calibration of UAV Hyperspectral Camera and Its Validation Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(02): 430-434. |
[6] |
YAN Peng, HU Bing-liang, LIU Xue-bin, SUN Wei, LI Li-bo, FENG Yu-tao, LIU Yong-zheng. Hadamard Transform Spectrometer Mixed Pixels’ Unmixing Method [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(10): 2870-2873. |
[7] |
GAO Hai-liang1, 2, 3, GU Xing-fa1, 3*, YU Tao1, 3, LI Xiao-ying1, 3, GONG Hui1, 2, 3, LI Jia-guo1, 2, 3, ZHU Guang-hui4 . HJ1A/HSI Radiometric Calibration and Spectrum Response Function Sensitivity Analysis [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(11): 3149-3155. |
[8] |
ZHANG Jun-qiang1,2, WU Qing-wen1, YAN Chang-xiang1 . Stray Light of Space-Borne Hyperspectral Imager and Its Measurement [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(10): 2861-2865. |
[9] |
LIANG Jiu-sheng1,2,WU Qing-wen1,LI Ze-xue3,CHEN Li-heng1,GUO Liang1 . Thermal Spectral Property of Prism in Hyper Spectral Imager [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(06): 1702-1706. |
[10] |
ZHOU Jin-song1, 2, XIANGLI Bin1, 3, WEI Ru-yi1 . Spatially Modulated Interference Hadamard Transform Spectral Imager[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(11): 3163-3166. |
[11] |
WU Di1, CHEN Xiao-jing1, 2, HE Yong1*. Application of Multispectral Image Texture to Discriminating Tea Categories Based on DCT and LS-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(05): 1382-1385. |
[12] |
CHEN Xiao-jing1,2,WU Di1,HE Yong1*,LIU Shou2. Study on Application of Multi-Spectral Image Texture to Discriminating Rice Categories Based on Wavelet Packet and Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(01): 222-225. |
[13] |
CHEN Xiao-jing1,WU Di2,HE Yong2*,LI Xiao-li2,LIU Shou1. Study on Discrimination of Tea Based on Color of Multispectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28(11): 2527-2530. |
[14] |
LI Qing-li1,XUE Yong-qi2,ZHANG Jing-fa3,LIU Zhi1. Microscopic Hyperspectral Image Study of Normal and Diabetic Retina Tissues of Rats[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(08): 1603-1606. |
|
|
|
|