Proceedings Article | 31 August 2009
Proc. SPIE. 7455, Satellite Data Compression, Communication, and Processing V
KEYWORDS: Image compression, Remote sensing, JPEG2000, Image registration, Image classification, Reconstruction algorithms, Earth observing sensors, 3D image processing, Affine motion model, High resolution satellite images
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.
