Special Section on Satellite Data Compression

Adaptive compression of remote sensing stereo image pairs

[+] Author Affiliations
Yunsong Li, Ruomei Yan, Chengke Wu, Keyan Wang

Xidian University, State Key Laboratory of Integrated Service Networks, No. 2, South Taibai Road, P.O. Box 103, Xi'an, Shaanxi 710071 China

Shizhong Li

Harbin Institute of Technology, School of Electronics and Information Technology, Harbin 150001 China

Yu Wang

Xi'an Research Institute of Surveying & Mapping, Xi'an 710054 China

J. Appl. Remote Sens. 4(1), 041777 (September 10, 2010). doi:10.1117/1.3495716
History: Received December 10, 2009; Revised August 3, 2010; Accepted September 2, 2010; September 10, 2010; Online September 10, 2010
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Abstract

According to the data characteristics of remote sensing stereo image pairs, a novel adaptive compression algorithm based on the combination of feature-based image matching (FBM), area-based image matching (ABM), and region-based disparity estimation is proposed. First, the Scale Invariant Feature Transform (SIFT) and the Sobel operator are carried out for texture classification. Second, an improved ABM is used in the flat area, while the disparity estimation is used in the alpine area. The radiation compensation is applied to further improve the performance. Finally, the residual image and the reference image are compressed by JPEG2000 independently. The new algorithm provides a reasonable prediction in different areas according to the image textures, which improves the precision of the sensed image. The experimental results show that the PSNR of the proposed algorithm can obtain up to about 3dB's gain compared with the traditional algorithm at low or medium bitrates, and the DTM and subjective quality is also obviously enhanced.

© 2010 Society of Photo-Optical Instrumentation Engineers

Citation

Yunsong Li ; Ruomei Yan ; Chengke Wu ; Keyan Wang ; Shizhong Li, et al.
"Adaptive compression of remote sensing stereo image pairs", J. Appl. Remote Sens. 4(1), 041777 (September 10, 2010). ; http://dx.doi.org/10.1117/1.3495716


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