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.

Multispectral imaging (MI) techniques are being used very often to identify different properties of nature in several domains, going from precision agriculture to environmental studies, not to mention quality inspection of pharmaceutical production, art restoration, biochemistry, forensic sciences or geology, just to name some. Different implementations are commercially available from the industry and yet there is quite an interest from the scientific community to spread its use to the majority of society by means of cost effectiveness and ease of use for solutions. These devices make the most sense when combined with unmanned aerial vehicles (UAVs), going a step further and alleviating repetitive routines which could be strenuous if traditional methods were adopted. In this work, a low cost and modular solution for a multispectral camera is presented, based on the use of a single panchromatic complementary metal oxide semiconductor (CMOS) sensor combined with a rotating wheel of interchangeable band pass optic filters. The system is compatible with open source hardware permitting one to capture, process, store and/or transmit data if needed. In addition, a calibration and characterization methodology has been developed for the camera, allowing not only for quantifying its performance, but also able to characterize other CMOS sensors in the market in order to select the one that best suits the budget and application. The process was experimentally validated by mounting the camera in a Dji Matrice 600 UAV to uncover vegetation indices in a reduced area of palm trees plantation. Results are presented for the normalized difference vegetation index (NDVI) showing a generated colored map with the captured information.

Real-time hyperspectral imaging on-board compression represents a critical processing step in many remote sensing applications where the acquired hyperspectral data need to be efficiently stored and/or transferred. However, the complexity of the compression algorithms as well as the volume of data to be compressed and the limited computational resources of the hardware devices available on-board turn the real-time compression into a very challenging task. This paper presents a low-power-consumption solution for real-time lossy compression of hyperspectral images. The lossy compression algorithm for hyperspectral image system (HyperLCA) compressor has been implemented onto two NVIDIA Jetson developer kits. These NVIDIA boards include low-power embedded graphic processing units, which allow parallel programing for speeding up the compression process at a reasonable low power consumption. The experiments carried out in this paper are oriented to the necessities imposed by a specific smart farming application although all drawn conclusions are extrapolable to other fields in which remotely sensed hyperspectral images are to be compressed in real time. The obtained results verify both the good performance of the HyperLCA compressor for the targeted application and the achievement of a real-time performance by using the developed implementations. Additionally, several comparisons and conclusions have been drawn from the experiments in relation to the different strategies employed for accelerating the compression process.

Hyperspectral Imaging (HSI) techniques have demonstrated potential to provide useful information in a broad set of applications in different domains, from precision agriculture to environmental science. A first step in the preparation of the algorithms to be employed outdoors starts at a laboratory level, capturing a high amount of samples to be analysed and processed in order to extract the necessary information about the spectral characteristics of the studied samples in the most precise way. In this article, a custom-made scanning system for hyperspectral image acquisition is described. Commercially available components have been carefully selected in order to be integrated into a flexible infrastructure able to obtain data from any Generic Interface for Cameras (GenICam) compliant devices using the gigabyte Ethernet interface. The entire setup has been tested using the Specim FX hyperspectral series (FX10 and FX17) and a Graphical User Interface (GUI) has been developed in order to control the individual components and visualise data. Morphological analysis, spectral response and optical aberration of these pushbroom-type hyperspectral cameras have been evaluated prior to the validation of the whole system with different plastic samples for which spectral signatures are extracted and compared with well-known spectral libraries.

Hyperspectral sensors that are mounted in unmanned aerial vehicles (UAVs) offer many benefits for different remote sensing applications by combining the capacity of acquiring a high amount of information that allows for distinguishing or identifying different materials, and the flexibility of the UAVs for planning different kind of flying missions. However, further developments are still needed to take advantage of the combination of these technologies for applications that require a supervised or semi-supervised process, such as defense, surveillance, or search and rescue missions. The main reason is that, in these scenarios, the acquired data typically need to be rapidly transferred to a ground station where it can be processed and/or visualized in real-time by an operator for taking decisions on the fly. This is a very challenging task due to the high acquisition data rate of the hyperspectral sensors and the limited transmission bandwidth. This research focuses on providing a working solution to the described problem by rapidly compressing the acquired hyperspectral data prior to its transmission to the ground station. It has been tested using two different NVIDIA boards as on-board computers, the Jetson Xavier NX and the Jetson Nano. The Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA) has been used for compressing the acquired data. The entire process, including the data compression and transmission, has been optimized and parallelized at different levels, while also using the Low Power Graphics Processing Units (LPGPUs) embedded in the Jetson boards. Finally, several tests have been carried out to evaluate the overall performance of the proposed design. The obtained results demonstrate the achievement of real-time performance when using the Jetson Xavier NX for all the configurations that could potentially be used during a real mission. However, when using the Jetson Nano, real-time performance has only been achieved when using the less restrictive configurations, which leaves room for further improvements and optimizations in order to reduce the computational burden of the overall design and increase its efficiency.

Most practical hyperspectral anomaly detection (AD) applications require real-time processing for detecting complex targets from their background. This is especially critical in defense and surveillance domains, but also in many other scenarios, in which a rapid response is mandatory to save human lives. Dealing with such a high dimensionality of data requires the conception of new algorithms to ease the demanding computing performance. Push-broom scanning represents the mainstream in hyperspectral imaging, introducing added complexity to the equation as there is no information of future pixels. In this paper, a novel technique named line-by-line anomaly detection (LbL-AD) algorithm, is presented as a way of performing real-time processing with a push-broom sensor. The sensor has been mounted on an unmanned aerial vehicle, and the acquired images, together with others from the scientific literature and synthetic ones, have been used to extensively validate the proposed algorithm in terms of accuracy, based on different metrics and processing time. Comparisons with state-of-the-art algorithms were accomplished in order to evaluate the goodness of the LbL-AD, giving as a result an outstanding performance.