As one of the most popular optical remote sensor images, MODIS (Moderate Resolution Imaging Spectroradiometer) image are widely used in many areas. However, the processing of MODIS image data is considered as a cumbersome, time-consuming work, especially for the long time series earth observation research. Automatic processing technology is specially needed here. But because of the complex procedure of image matching and the high requirement of location calibration, these images are manual processed in most of the researches. This paper presents an automatic processing method for MODIS image products (mainly for Level 1 B, can be applied on 8-day snow observation image product and daily snow cover optical image data as well). By using the automatic processing system, the efficiency of optical remote sensing image processing is sharply increased while the accuracy in calibration remains the same in comparing with traditional processing method. The working flowchart of the processing system is introduced for those who will deal with mass of MODIS data in their research. Finally, an automatic processing system of snow cover monitoring model based on MODIS L1B image data in ENVI/IDL environment is discussed as the practical application of processing method in long time series snow cover monitoring over Northeast China with MODSI images. The performance shows that the time spent in data processing can be saved from 48 manual working days to 2 working days( 10.41 hours) by computer automatic processing, which proves that processing efficiency of long time series remote sensing data, especial MODIS L1B data, can be greatly increased by saving processing time from months to days and researchers will have more free time from burdensome and automatic work by using the auto processing system.
In this paper, a modified algorithm was introduced to improve Rice coding algorithm and researches of image compression with the CDF (2,2) wavelet lifting scheme was made. Our experiments show that the property of the lossless image compression is much better than Huffman, Zip, lossless JPEG, RAR, and a little better than (or equal to) the famous SPIHT. The lossless compression rate is improved about 60.4%, 45%, 26.2%, 16.7%, 0.4% on average. The speed of the encoder is faster about 11.8 times than the SPIHT's and its efficiency in time can be improved by 162%. The speed of the decoder is faster about 12.3 times than that of the SPIHT's and its efficiency in time can be rasied about 148%. This algorithm, instead of largest levels wavelet transform, has high coding efficiency when the wavelet transform levels is larger than 3. For the source model of distributions similar to the Laplacian, it can improve the efficiency of coding and realize the progressive transmit coding and decoding.
An algorithm of combining LZC and arithmetic coding algorithm for image compression is presented and both theory deduction and simulation result prove the correctness and feasibility of the algorithm. According to the characteristic of context-based adaptive binary arithmetic coding and entropy, LZC was modified to cooperate the optimized piecewise arithmetic coding, this algorithm improved the compression ratio without any additional time consumption compared to traditional method.
In this paper the algorithms and its improvement of integer wavelet transform combining SPIHT and arithmetic coding in image lossless compression is mainly studied. The experimental result shows that if the order of low-pass filter vanish matrix is fixed, the improvement of compression effect is not evident when invertible integer wavelet transform is satisfied and focusing of energy property monotonic increase with transform scale. For the same wavelet bases, the order
of low-pass filter vanish matrix is more important than the order of high-pass filter vanish matrix in improving the property of image compression. Integer wavelet transform lossless compression coding based on lifting scheme has no relation to the entropy of image. The effect of compression is depended on the the focuing of energy property of image transform.
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