Hyperspectral imaging is known for its rich spatial–spectral information. The spectral bands provide the ability to distinguish substances spectra which are substantial for analyzing materials. However, high-dimensional data volume of hyperspectral images is problematic for data storage. In this paper, we present a lossy hyperspectral image compression system based on the regression of 3D wavelet coefficients. The 3D wavelet transform is applied to sparsely represent the hyperspectral images (HSI). A support vector machine regression is then applied on wavelet details and provides vector supports and weights which represent wavelet texture features. To achieve the best possible overall rate-distortion performance after regression, entropy encoding based on run-length encoding and arithmetic encoding is used. To preserve the spatial pertinent information of the image, the lowest sub-band wavelet coefficients are furthermore encoded by a lossless coding with differential pulse code modulation. Spectral and spatial redundancies are thus substantially reduced. Experimental tests are performed over several HSI from airborne and spaceborne sensors and compared with the main existing algorithms. The obtained results show that the proposed compression method has high performances in terms of rate distortion and spectral fidelity. Indeed, high PSNRs and classification accuracies, which could exceed 40.65 dB and $$75.8\%$$ 75.8 % , respectively, are observed for all decoded HSI images and overpass those given by many cited famous methods. In addition, the evaluation of detection and compression over various bands shows that spectral information is preserved using our compression method.

中文翻译：

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用于高光谱图像压缩的基于支持向量回归的 3D 小波纹理学习

高光谱成像以其丰富的空间光谱信息而闻名。光谱带提供了区分物质光谱的能力，这对于分析材料非常重要。然而，高光谱图像的高维数据量对于数据存储来说是有问题的。在本文中，我们提出了一种基于 3D 小波系数回归的有损高光谱图像压缩系统。3D 小波变换应用于稀疏表示高光谱图像 (HSI)。然后将支持向量机回归应用于小波细节并提供表示小波纹理特征的向量支持和权重。为了在回归后获得最佳的整体率失真性能，使用了基于游程编码和算术编码的熵编码。为了保留图像的空间相关信息，最低子带小波系数进一步通过具有差分脉冲编码调制的无损编码进行编码。频谱和空间冗余因此显着减少。对来自机载和星载传感器的几个 HSI 进行了实验测试，并与现有的主要算法进行了比较。所得结果表明，所提出的压缩方法在率失真和频谱保真度方面具有较高的性能。实际上，对于所有解码的 HSI 图像都观察到了高 PSNR 和分类精度，分别可能超过 40.65 dB 和 $$75.8\%$$ 75.8 % 并且超过了许多引用的著名方法给出的那些。此外，