Abstract-Transform-based lossy compression has a huge potential for hyperspectral data reduction. Hyperspectral data are 3-D, and the nature of their correlation is different in each dimension. This calls for a careful design of the 3-D transform to be used for compression. In this paper, we investigate the transform design and rate allocation stage for lossy compression of hyperspectral data. First, we select a set of 3-D transforms, obtained by combining in various ways wavelets, wavelet packets, the discrete cosine transform, and the Karhunen-Loève transform (KLT), and evaluate the coding efficiency of these combinations. Second, we propose a low-complexity version of the KLT, in which complexity and performance can be balanced in a scalable way, allowing one to design the transform that better matches a specific application. Third, we integrate this, as well as other existing transforms, in the framework of Part 2 of the Joint Photographic Experts Group (JPEG) 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. We introduce an evaluation framework based on both reconstruction fidelity and impact on image exploitation, and evaluate the proposed algorithm by applying this framework to AVIRIS scenes. It is shown that the scheme based on the proposed low-complexity KLT significantly outperforms previous schemes as to rate-distortion performance. As for impact on exploitation, we consider multiclass hard classification, spectral unmixing, binary classification, and anomaly detection as benchmark applications.

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Abstract-Transform-based lossy compression has a huge potential for hyperspectral data reduction. In this paper we propose a lossy compression scheme for hyperspectral data based on a new low-complexity version of the Karhunen-Loève transform, in which complexity and performance can be balanced in a scalable way, allowing one to choose the best trade off that better matches a specific application. Moreover, we integrate this transform in the framework of Part 2 of the JPEG 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard.

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We develop wavelet engines on a digital signal processors (DSP) platform, the target application being image and intraframe video compression by means of the forthcoming JPEG2000 and Motion-JPEG2000 standards. We describe two implementations, based on the lifting scheme and the filter bank scheme, respectively, and we present experimental results on code profiling. In particular, we address the following problems: (1) evaluating the execution speed of a wavelet engine on a modern DSP; (2) comparing the actual execution speed of the lifting scheme and the filter bank scheme with the theoretical results; (3) using the on-board direct memory access (DMA) to possibly optimize the execution speed. The results allow to assess the performance of a modern DSP in the image coding task, as well as to compare the lifting and filter bank performance in a realistic application scenario. Finally, guidelines for optimizing the code efficiency are provided by investigating the possible use of the on-board DMA

Hyperspectral image compression has recently attracted a remarkable interest for remote sensing applications. In this paper we propose a unified embedded lossy-to-lossless compression framework based on the JPEG 2000 standard. In particular, we exploit the multicomponent transformation feature of Part 2 of JPEG 2000 to devise a compression framework based on a spectral decorrelating transform followed by JPEG 2000 compression of the transformed coefficients. We evaluate several possible choices for the spectral transform, including a floating-point DCT, an integer DCT, and a wavelet transform. The final version of the proposed algorithm has been compared to 3D-SPIHT in the lossy case, and to several stateof-the-art compression algorithms including JPEG-LS and 3D-CALIC in the lossless case. Experimental results on AVIRIS data show that the proposed technique exhibits very competitive performance for both reversible and irreversible compression, with significantly lower complexity than DPCM-based methods, and memory requirements compatible with typical onboard processing subsystems of remote sensing platforms.

In this paper we propose a lossless compression algorithm for hyperspectral images based on distributed source coding; this algorithm represents a significant improvement over our prior work on the same topic, and has been developed during a project funded by ESA-ESTEC. In particular, the algorithm achieves good compression performance with very low complexity; moreover, it also features a very good degree of error resilience. These features are obtained taking inspiration from distributed source coding, and particularly employing coset codes and CRC-based decoding. As the CRC can be used to decode blocks using a reference different from that used to compress the image, this yields error resilience. In particular, if a block is lost, decoding using the closest collocated block in the second previous band is successful about 70% of the times