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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.

This paper presents an efficient and low-cost approach for energy analysis of road accidents using images obtained using consumer-grade digital cameras and smartphones. The developed method could be used by security forces in order to improve the qualitative and quantitative analysis of traffic accidents. This role of the security forces is crucial to settle arguments; consequently, the remote and non-invasive collection of accident related data before the scene is modified proves to be essential. These data, taken in situ, are the basis to perform the necessary calculations, basically the energy analysis of the road accident, for the corresponding expert reports and the reconstruction of the accident itself, especially in those accidents with important damages and consequences. Therefore, the method presented in this paper provides the security forces with an accurate, three-dimensional, and scaled reconstruction of a road accident, so that it may be considered as a support tool for the energy analysis. This method has been validated and tested with a real crash scene simulated by the local police in the Academy of Public Safety of Extremadura, Spain.

This paper presents a new approach for energetic analyses of traffic accidents against fixed road elements using close-range photogrammetry. The main contributions of the developed approach are related to the quality of the 3D photogrammetric models, which enable objective and accurate energetic analyses through the in-house tool CRASHMAP. As a result, security forces can reconstruct the accident in a simple and comprehensive way without requiring spreadsheets or external tools, and thus avoid the subjectivity and imprecisions of the traditional protocol. The tool has already been validated, and is being used by the Local Police of Salamanca (Salamanca, Spain) for the resolution of numerous accidents. In this paper, a real accident of a car against a fixed metallic pole is analysed, and significant discrepancies are obtained between the new approach and the traditional protocol of data acquisition regarding collision speed and absorbed energy