An efficient compression of hyperspectral images on-board satellites is mandatory in current and future space missions in order to save bandwidth and storage space. Reducing the data volume in space is a challenge that has been faced with a twofold approach: to propose new highly efficient compression algorithms; and to present technologies and strategies to execute the compression in the hardware available on-board. The Consultative Committee for Space Data Systems (CCSDS), a consortium of the major space agencies in the world, has recently issued the CCSDS 123 standard for multispectral and hyperspectral image (MHI) compression, with the aim of facilitating the inclusion of on-board compression on satellites by the space industry. In this paper, we present a low-complexity feld programmable gate arrays (FPGAs) implementation of this recent CCSDS 123 standard, which demonstrates its main features in terms of compression efficiency and suitability for an implementation on the available on-board technologies. A hardware architecture is conceived and designed with the aim of achieving low hardware occupancy and high performance on a space-qualified FPGA from the Microsemi RTAX family. The resulting FPGA implementation is therefore suitable for on-board compression. The effect of the several CCSDS-123 configuration parameters on the compression efficiency and hardware complexity is taken into consideration to provide flexibility in such a way that the implementation can be adapted to different application scenarios. Synthesis results show a very low occupancy of 34% and a maximum frequency of 43 MHz on a space-qualified RTAX1000S. The benefits of the proposed implementation are further evidenced by a demonstrator, which is implemented on a commercial prototyping board from Xilinx. Finally, a comparison with other FPGA implementations of on-board data compression algorithms is provided.

Linear spectral unmixing is nowadays an essential tool to analyze remotely sensed hyperspectral images. Although many different contributions have been uncovered during the last two decades, the majority of them are based on dividing the whole process of linearly unmixing a given hyperspectral image into three sequential steps: 1) estimation of the number of endmembers that are present in the hyperspectral image under consideration; 2) extraction of these endmembers from the hyperspectral data set; and 3) calculation of the abundances associated with the endmembers induced in the previous step per each mixed pixel of the image. Although this de facto processing chain has proven to be accurate enough for unmixing most of the images collected by hyperspectral remote sensors, it is also true that it is not exempt of drawbacks, such as the fact that all the possible combinations of algorithms in order to fully unmix a hyperspectral image according to the aforementioned processing chain demand a formidable computational effort, which tends to be higher the better the performance of the designed unmixing chain is. This troublesome issue unfortunately prevents the use of hyperspectral imaging technology in applications under real-time constraints, in which hyperspectral images have to be analyzed in a short period of time. Hence, there is a clear need to face the challenge of fully exploiting the unquestionable benefits of the hyperspectral imaging technology for these applications, but concurrently overcoming the limitations imposed by the computationally complex nature of the processes involved. For this purpose, this paper introduces a novel algorithm named fast algorithm for linearly unmixing hyperspectral images (FUN), which is capable of fully unmixing a hyperspectral image with at least the same accuracy than state-of-the-art approaches while demanding a much lower computational effort, independent of the characteristics of the image under analysis. The FUN algorithm is based on the concept of orthogonal projections and allows performing the estimation of the number of endmembers and their extraction simultaneously, using the modified Gram-Schmidt method. The operations performed by the FUN algorithm are simple and can be easily parallelized. Moreover, this algorithm is able to calculate the abundances using very similar operations, also based on orthogonal projections, which makes it easier to achieve a hardware implementation to perform the entire unmixing process. The benefits of our proposal are demonstrated with a diverse set of artificially generated hyperspectral images and with the well-known AVIRIS Cuprite image, for which the proposed FUN algorithm is able to reduce in a factor of more than 31 times the time required for processing it, while providing a better unmixing performance than traditional methods.

In this paper, a performance evaluation of the state-of-the-art H.264/AVC video coding standard is carried out with the aim of determining its feasibility when applied to hyperspectral image compression. Results are obtained based on configuring diverse parameters in the encoder in order to achieve an optimal trade-off between compression ratio, accuracy of unmixing and computation time. In this sense, simulations are developed in order to measure the spectral angles and signal-to-noise ratio (SNR), achieving compression ratios up to 0.13 bits per pixel per band (bpppb) for real hyperspectral images. Moreover, in this work it is detected which blocks in the encoder contribute the most to performance improvements of the compression task for the particular case of this type of images, and which ones are not relevant at all and hence could be removed. This conclusion yields to reduce the future design complexities of potential low-power/real-time hyperspectral encoders based on H.264/AVC for remote sensing applications.

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.

This paper presents the modelling, design and implementation of two IP cores that are compliant with the 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 FPGA or ASIC of interest in the space environment, aiming to perform efficiently compression in satellites despite the inherent constraints of these devices. Results are reported as implementation results for the FPGA devices that are currently considered most representative and suited for on-board use from Xilinx and Microsemi families. In particular, the CCSDS123 IP core reaches for the Virtex5 FX130 a throughput of 153.5 Msamples per second, comprising only 5% of the LUTs in the device.

Graphics Processing Units (GPU) are becoming a widespread tool for general-purpose scientific computing, and are attracting interest for future onboard satellite image processing payloads due to their ability to perform massively parallel computations. This paper describes the GPU implementation of an algorithm for onboard lossy hyperspectral image compression, and proposes an architecture that allows to accelerate the compression task by parallelizing it on the GPU. The selected algorithm was amenable to parallel computation owing to its block-based operation, and has been optimized here to facilitate GPU implementation incurring a negligible overhead with respect to the original single-threaded version. In particular, a parallelization strategy has been designed for both the compressor and the corresponding decompressor, which are implemented on a GPU using Nvidia's CUDA parallel architecture. Experimental results on several hyperspectral images with different spatial and spectral dimensions are presented, showing significant speed-ups with respect to a single-threaded CPU implementation. These results highlight the significant benefits of GPUs for onboard image processing, and particularly image compression, demonstrating the potential of GPUs as a future hardware platform for very high data rate instruments.