1 September 2010 Adaptive compression of remote sensing stereo image pairs
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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.
Yunsong Li, Yunsong Li, Ruomei Yan, Ruomei Yan, Chengke Wu, Chengke Wu, Keyan Wang, Keyan Wang, Shizhong Li, Shizhong Li, Yu Wang, Yu Wang, } "Adaptive compression of remote sensing stereo image pairs," Journal of Applied Remote Sensing 4(1), 041777 (1 September 2010). https://doi.org/10.1117/1.3495716 . Submission:
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