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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.

Application-oriented solutions based on the combination of different technologies such as unmanned aerial vehicles (UAVs), advanced sensors, precise GPS, and embedded devices have led to important improvements in the field of cyber-physical systems. Agriculture, due to its economic and social impact on the global population, arises as a potential domain which could enormously benefit from this paradigm in terms of savings in time, resources and human labor, not to mention aspects related to sustainability and environment respect. This has led to a new revolution named precision agriculture (or precision farming), based on observing and measuring inter and intra-field variability in crops. A key technology in this scenario is the use of hyperspectral imaging, firstly used in satellites and later in manned aircraft, composed by hundreds of spectral bands which facilitate hidden data to be converted into useful information. In this paper, a hyperspectral flying platform is presented and the construction of the whole system is detailed. The proposed solution is based on a commercial DJI Matrice 600 drone and a Specim FX10 hyperspectral camera. The challenge in this work has been to adopt this latter device, mainly conceived for industrial applications, into a flying platform in which weight, power budget, and connectivity are paramount. Additionally, an embedded board with advanced processing capabilities has been mounted on the drone in order to control its trajectory, manage the data acquisition, and allow on-board processing, such as the evaluation of different vegetation indices (the normalized difference vegetation index, NDVI, the modified chlorophyll absorption ratio index, MCARI, and the modified soil-adjusted vegetation index, MSAVI), which are numerical and/or graphical indicators of the vegetation properties and compression, which is of crucial relevance due to the huge amounts of data captured. The whole system was successfully tested in a real scenario located on the island of Gran Canaria, Spain, where a vineyard area was inspected between May and August of the year 2018.INDEX TERMS Unmanned aerial vehicle, hyperspectral, pushbroom sensor, vegetation index, on-board processing.

Currently, the use of hyperspectral imaging (HSI) for the inspection of microscopic samples is an emerging trend in different fields. The use of push-broom hyperspectral (HS) cameras against other HSI technologies is motivated by their high spectral resolution and their capabilities to exploit spectral ranges beyond 1000 nm. Nevertheless, using push-broom cameras in miscroscopes imposes to perform an accurate spatial scanning of the sample to collect the HS data. In this manuscript, we present a methodology to correctly set-up a push-broom HS microscope to acquire high-quality HS images. Firstly, we describe a custom 3D printed mechanical system developed to perform the spatial scanning by producing a precise linear movement of the microscope stage. Then, we discuss how the dynamic range maximisation, the focusing, the alignment and the adequate speed determination affect the overall quality of the images. Finally, we present some examples of HS data showing the most common defects that usually appear when capturing HS images using a push-broom camera, and also a set of images acquired from real microscopic samples.INDEX TERMS Hyperspectral imaging, microscopy, system analysis and design, data acquisition, image analysis, image enhancement.

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Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA), suffer from their computationally demanding nature, becoming advisable for their implementation onto high-performance computer architectures for applications under strict latency constraints. This work presents the implementation of the PCA algorithm onto two different high-performance devices, namely, an NVIDIA Graphics Processing Unit (GPU) and a Kalray manycore, uncovering a highly valuable set of tips and tricks in order to take full advantage of the inherent parallelism of these high-performance computing platforms, and hence, reducing the time that is required to process a given hyperspectral image. Moreover, the achieved results obtained with different hyperspectral images have been compared with the ones that were obtained with a field programmable gate array (FPGA)-based implementation of the PCA algorithm that has been recently published, providing, for the first time in the literature, a comprehensive analysis in order to highlight the pros and cons of each option.

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