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Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth's surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA), is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios.

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This paper presents the modeling, design, and implementation of two intellectual property (IP) cores that are compliant with the consultative committee for space data systems (CCSDS) 121.0-B-2 and CCSDS 123.0-B-1 lossless satellite image compression standards. The CCSDS 121.0-B-2 describes a lossless universal compressor based on a Rice adaptive encoding. The CCSDS 123.0-B-1 standard describes a lossless algorithm specifically designed for efficient on-board compression of hyperspectral and multispectral images, and it is based on a prediction and entropy-based encoding structure. Two options are offered for the latter: the sample-adaptive and the block-adaptive encoder, which corresponds to the CCSDS 121.0-B-2 algorithm. These IP cores have been designed as independent compressors, but they can be easily combined in a plug-and-play fashion to be used together thanks to a dedicated interface. Additionally, standard interfaces are provided for configuration and external memory access. The design process encompasses the consideration of several different hardware architectures in order to maximize throughput and optimize the requirements of on-board resources at the same time. Both IPs are compliant with the high degree of configurability considered in the standard. The obtained VHDL code is completely technology independent, so it can be used to target any field-programmable gate array (FPGA) or ASIC of interest in the space environment, aiming to perform efficiently compression in satellites despite the inherent