On the basis of analyzing the characteristics of the stereo image pair obtained by the remote sensing mapping,
considering the continual increment of data obtained by stereo mapping satellite on orbit and the trend of the hardware
development, a joint compression algorithm for the stereo image pairs based on the classified characters is proposed.
First, stereo image pairs are matched through SIFT feature matching; and then the images are classified adaptively by the
features according to the matching results. For the region where the characteristics are flat, the predicted residual-error
image is coded based on the coarse match and the fine match using the adaptive block in order to improve the accuracy
of matching with the linear prediction for the radiation compensation. For the feature-rich region, according to the
correlation of the images, individual compression or joint compression is selected. When the joint compression algorithm
is selected, the match alignment of the left and right image pair is done by the affine model estimation and the linear
prediction is adapted to the radiation compensation. Because different compression scheme is adopted for different
characteristics regions, the results show that the PSNR and the geometric precision of the reconstructed images are
improved effectively, especially when the images contain many characteristics of elevation information.
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.
With the development of spatial, temporal and spectral resolution of remote satellite imagery, the data acquired in the
orbit must be compressed to meet the requirement of the real-time data downlink transmission. When the satellite data
are transmitted in the downlink space channel, error code may be produced as a result of external interference. The error
code will cause serious diffusion problem for the transmission of compressed data. According to the principle of the
error control encoding and decoding, as well as the corresponding recommends of consultative committee for space data
systems (CCSDS), a set of error control algorithm and scheme is proposed to satisfy the requirement of remote sensing
satellite data transmission, combining the data compression practice. The emulation experiments show that the method is
effective for preventing and reducing the error diffusion, so as to provide reliable data for the technique processing in the
fields of remote sensing.
Peak signal-to-noise ratio (PSNR) is commonly used as an objective metric in evaluating image quality. However, PSNR
can not reflect the visual perception distortion, especially for the stereo image in mapping. Meanwhile, the subjective
evaluation methods have great uncertainty. So, how to make correct, effective and repeatable evaluation for the stereo
mapping imagery is still a problem to be resolved. From the aspect of practical mapping application for the stereo
surveying and mapping satellite imagery, a multidimensional model for evaluating the stereo imagery compression
quality is presented in this paper. A new quality assessment index is proposed for decompressed image based on the
model, and by using it, the quality of imagery compressed /decompressed by JPEG and JPEG 2000 algorithm is
evaluated. Experiment results show that using this method can get better consistency with the result from the human
visual perception, and has high correlation with the method of mean opinion score(MOS), superior to the method of
using PSNR.
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.
Because of the unique geometry and radiometric characteristics of linear CCD satellite image pairs, methods
developed for aerial or perspective image pairs cannot be applied. This paper proposed stereo matching algorithms taking
the epipolar line of linear pushbroom sensors and scene geometry into account for satellite images. Firstly, the author
puts forward the approximate line constraint method of dynamic epipolar line, and sets up constraint conditions of
epipolar line in linear CCD stereo image matching, and proposes an imaging constraint method on the basis of
application analysis of epipolar line, with imaging characteristics taken into account. The method can eliminate the
distortion of geometry and make the primitives more prominent. At last we assess the performance of our strategy using
real satellite image data. The results show we can increase the accuracy of image match and minimize the computation
time with these techniques.
With the development of modern small satellite technology, it is potential and significant to use the new technology for the earth observation. The stereo mapping micro-satellite--"Experiment Satellite 1", designed and built by Harbin Institute of Technology, is taken as an example for discussion of the relevant technologies and corresponding principle of the satellite payload and its ground application system. The ground application system receives the stereo photographs data from the "Experiment Satellite 1" and then produces surveying products such as digital elevation map, digital orthoimage and digital topographic map, etc. "Experiment Satellite 1" is the first satellite where three-line-array CCD camera is used triumphantly in world for stereo mapping. It will have profound influence on the earth observation technique.
A new reliable multicast transport protocol SM_TCP is proposed for satellite IP networks in this paper. In SM_TCP, the XOR scheme with the aid of on-board buffering and processing is used for error recovery and an optimal retransmission algorithm is designed, which can reduce the recovery time by half of the RTT and minimize the number of retransmissions. In order to avoid the unnecessary decrease of congestion window in the high BER satellite channels, the occupied buffer sizes at bottlenecks are measured in adjusting the congestion window, instead of depending on the packet loss information. The average session rate of TCP sessions and of multicast sessions passing through the satellite are also measured and compared in adjusting the congestion window, which contributes to bandwidth fairness. Analysis and simulation results show fairness with TCP flows and scalability.
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