A BOI-Preserving-Based Compression Method for Hyperspectral Images
A BOI-Preserving-Based Compression Method for Hyperspectral Images
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DOI：
10.1109/tgrs.2010.2070511
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发表时间：
2010-09
影响因子：
8.2
通讯作者：
Hao Chen;Ye Zhang;Junping Zhang;Yushi Chen
中科院分区：
文献类型：
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作者：
Hao Chen;Ye Zhang;Junping Zhang;Yushi Chen
高光谱图像通常包含数百个波段，这些波段在应用中具有不同的重要性。大多数HSI压缩方法通常以相同的方式处理大多数波段，而没有考虑到不同波段的差异，这可能会导致重要的光谱信息丢失。为了在应用中保留感兴趣的频谱信息，提出了一种新的基于感兴趣带（BOI）保存的HSI压缩方法。提出BOI的概念是因为在特定应用中某些波段具有显著性，并根据应用需求选择BOI的选择方法。首先根据应用测量进行BOI选择。然后，将BOI信息输入到递归双向预测（RBP）和分层树集合分割（SPIHT）压缩方案中，该压缩方案使用RBP进行光谱去相关，然后使用SPIHT算法对去相关残差图像进行编码。通过两种方法分别将更多的比特分配给BOI以保护BOI。分别以低失真和高失真直接压缩BOI和非BOI频带，并压缩所有低失真频带并执行压缩后截断。实验采用不同设置的AVIRIS图像进行。结果表明，两种方法均能获得较好的压缩效率和重构质量。此外，它们可以提高在材料分类和目标识别方面的应用效果。与非boi压缩算法相比，在压缩比为80时，所提方法的分类准确率提高2%，目标识别准确率提高9%。
Hyperspectral images (HSI) regularly contain hundreds of bands, which are of different importance in the application. Most HSI compression methods usually deal with most bands in the same way, and they do not take the difference of different bands into consideration, which may cause the loss of important spectral information. In order to preserve the spectral information of interest for applications, a new band-of-interest (BOI)-preserving-based HSI compression method is proposed. The conception of BOI is proposed because some bands are significant in the specific applications, and BOI selection methods are chosen according to application requirements. BOI selection is first performed according to application measurements. Then, BOI information is fed into recursive bidirection prediction (RBP) and set partition in hierarchical trees (SPIHT) compression scheme which uses RBP for spectral decorrelation followed by SPIHT algorithm for coding the resulting decorrelated residual images. More bits are allocated to BOI to preserve BOI by two approaches, respectively. Compress BOI and non-BOI bands directly with low distortion and high distortion, respectively, and compress all bands with low distortion and perform a postcompression truncation. Experiments are implemented with different settings using AVIRIS images. Results indicate that the proposed two methods both can achieve excellent compression efficiency and reconstructed quality. In addition, they can improve the application effect in both material classification and target recognition. Compared with non-BOI compression algorithm, at the compression ratio of 80, the proposed methods improve the classification accuracy by 2% and target recognition accuracy by 9%.