Hyperspectral Image Compression Algorithm with Maximum Error Controlled Based on Clustering
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摘要: 針對原有基于奇異值分解的最大誤差可控的高光譜圖像壓縮(EC-SVD)算法未充分利用圖像光譜矢量間冗余的問題，該文將高光譜圖像壓縮與聚類結(jié)合，提出最大誤差可控的高光譜圖像聚類壓縮算法。分析發(fā)現(xiàn)，圖像的光譜矢量間相似度越高越有利于得到好的最終壓縮效果。因此，算法首先使用K-均值聚類對高光譜圖像像元按光譜矢量聚類，以提高同類光譜矢量間的相似度；其次，對每一類像元分別使用EC-SVD算法思想壓縮以控制最大誤差。論文證明了當高光譜圖像的像元個數(shù)與波段數(shù)之比較大，且聚類類數(shù)不大于8時，聚類能夠提高圖像最終壓縮比。最后，設(shè)計整體壓縮實驗仿真流程，并對實際高光譜圖像進行數(shù)值仿真。結(jié)果表明，在相同參數(shù)條件下，該文算法比EC-SVD算法得到的壓縮比和信噪比均有提高，最大壓縮比提高了10% 左右。該文算法能夠有效提高EC-SVD算法的圖像壓縮效果。Abstract: Aiming at the problem that the maximum Error Controllable compression based on SVD (EC-SVD) algorithm can not make full use of spectral vectors redundancy in hyperspectral image, a hyperspectral image compression algorithm with maximum error controlled based on clustering is presented in this paper, by combining hyperspectral image compression with clustering. It is found that a higher compression ratio can be achieved as spectral vectors similarity increases. Using the K-means clustering algorithm, the pixels of hyperspectral image are clustered by spectral vectors to improve the similarity of spectral vectors in the same class. Then, the pixels in each class are compressed using the idea of EC-SVD algorithm. And it is shown that the compression ratio increases if the cluster number is no more than 8 and the number of pixels is larger than that of bands in the clustered hyperspectral image. Finally, a total simulation procedure of the improved compression algorithm is designed and some hyperspectral images are tested. The results of the tests show that compression ratios and signal to noise ratios are higher than those of EC-SVD algorithm under the same parameters; the maximum compression ratio rises around 10 percent. The presented improved algorithm can raise the compression efficiencies of hyperspectral images.
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Key words:
- Hyperspectral image /
- Image compression /
- Error controllable /
- Clustering
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