Lossless Compression of Hyperspectral Images Using K-means Clustering and Conventional Recursive Least-squares Predictor
Funds:
The National Natural Science Foundation of China (41101419)
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摘要: 針對基于預測的高光譜圖像無損壓縮算法壓縮比低的問題，該文將聚類算法與高光譜圖像預測壓縮算法相結合，提出一種基于K-均值聚類和傳統(tǒng)遞歸最小二乘法的高光譜圖像無損壓縮算法。首先，對高光譜圖像按光譜矢量進行K-均值聚類以提升同類光譜矢量間的相似度。然后，對每一聚類群分別使用傳統(tǒng)遞歸最小二乘法進行預測，消除高光譜圖像的空間冗余和譜間冗余。最后，對預測誤差圖像進行算術編碼，完成高光譜圖像壓縮過程。對AVIRIS 2006高光譜數(shù)據(jù)進行仿真實驗，所提算法對16位校正圖像、16位未校正圖像和12位未校正圖像分別取得了4.63倍，2.82倍和4.77倍的壓縮比，優(yōu)于同類型已報道的各種算法。Abstract: To improve the compression ratio of lossless compression scheme based on prediction, a lossless compression scheme for hyperspectral images using K-means Clustering method and Conventional Recursive Least-Squares (C-CRLS) predictor is presented in this paper. The proposed scheme first clusters the spectral data into clusters according to their spectra using the famous K-means clustering method. Then, the proposed scheme calculates the preliminary estimates to form the input vector of the conventional recursive least-squares predictor. Finally, after prediction, the prediction residuals are sent to the arithmetic coder. Experiments on the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 2006 hyperspectral images show that the proposed scheme yields an average compression ratio of 4.63, 2.82, and 4.77 on the 16-bit calibrated images, the 16-bit uncalibrated images, and the 12-bit uncalibrated images, respectively. Experimental results demonstrate that the proposed scheme outperforms other current state-of-the-art schemes for hyperspectral images that have been previously reported.
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Key words:
- Hyperspectral images /
- Image compression /
- Recursive least-squares /
- Clustering
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