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.

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.

The HyperLCA compressor is a transform based algorithm specifically designed for the real-time compression of hyperspectral images captured by pushbroom scanners, using limited computational resources. It is based on the HyperLCA Transform, which follows an unmixing-like strategy to independently compress each hyperspectral frame in a causal manner. A novel approach with respect to the original HyperLCA Transform is introduced in this work. By reusing the information used to compress one frame in the subsequent frames, it has been possible to increase the HyperLCA Transform compression performance and to reduce its computational burden. Additionally, the proposed approach is applicable not only to the targeted compressor but also to other causal hyperspectral analysis algorithms based on orthogonal projections and/or unmixing like strategies. The proposed solution has been tested in a real UAV-based acquisition platform, demonstrating the ability of our proposal to compress and transmit the captured hyperspectral data to a ground station in real-time.

New trends in the development of tools and strategies to optimize the productivity of agricultural fields have led to exciting opportunities based on a combination of two key technologies: Unmanned Aerial Vehicles (UAVs) and spectral sensors. Both of them come from different domains, the former playing a significant role mainly in military applications, and the latter in the space sector. UAVs and earth observation satellites represent the two extremes of monitoring systems in which airborne and High Altitude Pseudo Satellites (HAPS) are located in the middle range. From these four options, UAVs represent a good compromise between cost, performance and ease of operation in their numerous civil and environmental applications. However, they are also very challenging mainly due to the integration of specific payload which requires engineering experience in optics, electronics, physics and aerodynamics. In this paper, a hyperspectral UAV platform is presented, detailing every decision adopted since its inception for its use in precision agriculture, solving the problems encountered in its construction and developing an ad-hoc application to control the flight trajectory and manage data acquisition optimizing its battery limitations. The result is a robust autonomous system tested in a real scenario to monitor the health status of a vineyard in just a few minutes, based on different Vegetation Indices (VIs), reducing the time and efforts employed by traditional farmers and opening new avenues in the management of agricultural production and environmental respect.