Remotely piloted aircraft systems (RPAS) have introduced a new ability to quickly deploy low-cost, fully or partially autonomous aerial sensor platforms, which has created new intelligence, surveillance, and reconnaissance capabilities in various domains using cameras that are ubiquitous in most RPAS. The mounted cameras acquire images or full-motion video (FMV) which can be analyzed using object detection algorithms for locating and classifying one or more specified targets. To date, there has not been much published work regarding the effect of the RPAS flight parameters on the performance of object detection algorithms. To explore the use of object detection on aerial FMV acquired at various RPAS flight parameter settings, a dataset acquisition campaign was launched resulting in 8.5 h of RPAS-acquired FMV. Analysis and interpretation of the acquired dataset revealed that state-of-the-art performance was achieved using a modified you only look once object detection algorithm when the RPAS was deployed under an altitude of 30 m, at a velocity of under 7 m/s, and at pitch angles ranging from 25 deg to 65 deg while acquiring FMV at a resolution of 4.16 MP. The experimental results show that, when flown under specific conditions, RPAS are an effective and reliable platform for acquiring aerial FMV for the purpose of object detection which has a variety of different applications, such as peace support, public safety, and aerial monitoring.
The uniqueness and complexity of geological settings create challenges for hyperspectral sensing applications in mineral exploration. The optimization of an airborne hyperspectral survey design is a necessary step providing constraints on spectral signatures of the target, sensors used, and best flight parameters. Herein, we present a versatile software tool called HYSIMU that can be used to simulate different flight/sensor scenarios using mineral reflectance spectra from the USGS spectral library and find the most optimum survey design for a specific surface target. Random or selectable mineral reflectance spectra were assigned to synthetic random fields to create mineral distributions on synthetic digital elevation models with added spatial noise, spectral noise, and sun shadow effect. Several flights were simulated with various flight parameters such as altitude (10-200 meters), speed (1-100 m/s), and sun elevation (0, 45, 90 degrees). Several target scenarios with different levels of complexity and a 224-band hyperspectral sensor were simulated. The synthetic hyperspectral data generated from each flight and scenario were classified using ENVI. A sensitivity study was done by comparing the classification maps obtained and the ground truth using four different methods: Hausdorff Distance, Structural Similarity Index, Equal Pixel Percentage, and Correlation Coefficient. The results show a dependency of the classification maps on altitude (best for 10-50 meters) and slow flight speeds (best for 1-10 m/s) while the sun elevations did not cause any observed change in this altitude range. The results show that this toolkit can simulate any type of mineral exploration target from various airborne platforms and different flight parameters towards optimization of hyperspectral surveys.
The next generation of multi-domain airborne platforms will provide military operators with unparalleled sensor data streams spanning video, radar, and other sensor inputs. These expanded sensor capabilities will substantially increase access to critical, near-real time surveillance. However, the process of interpreting video feeds places a significant burden on intelligence operators, a demand that can be addressed by AI-based algorithms. AI-based tools complement the aerial footage processing tasks performed by full motion video analysts. In this work, we introduce a new aerial pattern of life dataset and describe our latest algorithmic developments, which uses deep learning to gain an understanding of a scene’s patterns of life. This approach allows anomalies, outliers from standard patterns of life, to be identified using supervised and unsupervised learning approaches. Herein, we describe our deep learning models and our corresponding microservices software architecture. The patterns of life and anomaly detection performance is measured through analysis of video from this new remotely piloted aerial system (RPAS) flight campaign dataset.
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