Region-based compression of remote sensing stereo image pairs

Ruomei Yan; Yunsong Li; Chengke Wu; Keyan Wang; Shizhong Li

doi:10.1117/12.825631

31 August 2009 Region-based compression of remote sensing stereo image pairs

Ruomei Yan, Yunsong Li, Chengke Wu, Keyan Wang, Shizhong Li

Proceedings Volume 7455, Satellite Data Compression, Communication, and Processing V; 74550Q (2009) https://doi.org/10.1117/12.825631
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

Abstract

According to the data characteristics of remote sensing stereo image pairs, a novel 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 area with flat terrain (flat area), while the disparity estimation, a combination of quadtree decomposition and FBM, is used in the area with alpine terrain (alpine area). Furthermore, the radiation compensation is applied in every area. Finally, the disparities, the residual image, and the reference image are compressed by JPEG2000 together. The new algorithm provides a reasonable prediction in different areas according to characteristics of 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 subjective quality is obviously enhanced.

Citation Download Citation

Ruomei Yan, Yunsong Li, Chengke Wu, Keyan Wang, and Shizhong Li "Region-based compression of remote sensing stereo image pairs", Proc. SPIE 7455, Satellite Data Compression, Communication, and Processing V, 74550Q (31 August 2009); https://doi.org/10.1117/12.825631

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KEYWORDS

Image compression

Remote sensing

Affine motion model

Image classification

Earth observing sensors

High resolution satellite images

JPEG2000

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