ABSTRACT Sparse representation provides an efficient way for the compression of hyperspectral images in the literature. In this work, an improved version of the Spectral-Spatial Adaptive Sparse Representation (SSASR), Modified SSASR (MSSASR), is proposed for hyperspectral image compression. In the first step of the proposed method, superpixel maps are generated for adaptive spatio-spectral representation. Then, the best possible dictionary is computed for the representation of the data. Afterwards, sparse coefficients are determined depending on the dictionary by Simultaneous Orthogonal Matching Pursuit (SOMP) method. In the final step, the dictionary and sparse coefficients are encoded by quantization and entropy encoding. This paper has the following novelties: modified dictionary learning step, new ordering scheme and Differential Pulse Code Modulation (DPCM) usage. Owing to modified dictionary learning, the sparse coefficients can be represented more compact than traditional SSASR. By using of new ordering scheme, it is not needed to send the superpixel map as side information. Moreover, DCPM usage lowers the magnitudes of sparse coefficients. Thanks to these modifications, the proposed method achieves an important improvement on compression performance. In the experimental results, the proposed method is compared with PCA+JPEG2000, DWT+JPEG2000, 3D-SPECK, 3D-TARP and SSASR methods on Indian Pines, Washington DC Mall, Jasper Ridge and Moffett Field scenes. The evaluation is carried out not only using distance and similarity metrics, namely, signal-to-noise ratio, mean spectral angle and mean spectral correlation metrics but also computation times. Additionally, reconstruction quality in anomaly regions is also used for comparison. Experimental results show that the proposed method outperforms the other compression methods in terms of quality metrics and anomaly preserving performance.

中文翻译：

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改进字典和稀疏表示的基于超像素的高光谱图像压缩

摘要 稀疏表示为文献中的高光谱图像压缩提供了一种有效的方法。在这项工作中，提出了用于高光谱图像压缩的改进版本的光谱空间自适应稀疏表示 (SSASR)，即修改的 SSASR (MSSASR)。在所提出方法的第一步中，为自适应空间光谱表示生成超像素图。然后，计算出最好的字典来表示数据。然后，通过同时正交匹配追踪（SOMP）方法根据字典确定稀疏系数。在最后一步，字典和稀疏系数通过量化和熵编码进行编码。本文有以下新颖之处：修改字典学习步骤，新的排序方案和差分脉冲编码调制 (DPCM) 用法。由于修改了字典学习，稀疏系数可以比传统的 SSSR 更紧凑地表示。通过使用新的排序方案，不需要将超像素图作为辅助信息发送。此外，DCPM 的使用降低了稀疏系数的大小。由于这些修改，所提出的方法实现了压缩性能的重要改进。在实验结果中，该方法与PCA+JPEG2000、DWT+JPEG2000、3D-SPECK、3D-TARP和SSASR方法在Indian Pines、Washington DC Mall、Jasper Ridge和Moffett Field场景上进行了比较。评估不仅使用距离和相似性度量，即信噪比，平均光谱角度和平均光谱相关性指标以及计算时间。此外，异常区域的重建质量也用于比较。实验结果表明，所提出的方法在质量指标和异常保持性能方面优于其他压缩方法。