Award Data

Tomographic SAR is a data collection and processing technology that produces true 3-D volumetric imagery. This approach offers significant benefits for enhanced imagery through clutter rejection, enhanced target definition, and better resolution. Tomographic SAR has been demonstrated at UHF frequencies where it has shown benefits in concealed target detection, improved feature extraction and cha ...

To detect uncommon targets in remote sensing imagery, it is quite often that very few prior examples are available. This so-called low-shot detection remains a very challenging problem in remote sensing, despite the recent development in state-of-the-art object detection algorithms such as Faster R-CNN and YOLO, and low-shot learning methods such as feature shrinking, model regression and memory a ...

Following technology advances in high-performance computation systems and fast growth of data acquisition, a technical breakthroughnamed Deep Learning made remarkable success in many research areas and applications. Nevertheless, the progress of hardwaredevelopment still falls far behind the upscaling of deep neural network (DNN) models at the software level. NGA seeks to apply neuralnetwork minia ...

Topographic features found in ground-based natural images contain information that is useful for a variety of applications including locationestimation and navigation. Traditionally these features have been manually labeled by analysts which is costly and time consuming, especiallyconsidering the volume of readily available data. We propose a novel method for extracting topographic features from s ...

With the explosive growth of Internet of Things (IoT) and mobile communication technologies, media streaming service and applications basedon video content have gained remarkable popularity and interest from users. When someone is using their device to record a video, then sharethat video with friends through a certain website (e.g. Netflix, YouTube), the process may sound simple from the user sid ...

The objective of this project is to design a software architecture based on densely-connected neural network to perform automatic targetsegmentation and recognition using training datasets of limited size (low-shot). Deep learning architectures have proved to be extremelyeffective at object detection and recognition, but such capability comes at the cost of having large labeled datasets. Such data ...

Next Century Corporation proposes the development of Muggsy, a low-shot deep learning detection prototype system that learns to recognize uncommon targets in remote imagery. Our Phase I research extends and leverages an image classification system of our own design called EvoDevo. EvoDevo evolves its own neural network architecture before training to meet the complexity of the data. Muggsy uses le ...

Next Century Corporation proposes to create TopoRobo (topographical annotation robot software) to automatically extract depth-orderedlists of ridge polylines overlaid on an image or video mosaics to feed topography-based geolocation algorithms. TopoRobo will leverage deepneural network machine learning methods optimized for topographical features through EvoDevo, Next Centurys algorithm to grow an ...

To maximize the utility of spectral imagery sensors it is essential that processing techniques for data from these sensors are highly automated, provide high probability of target detection with low false alarm rates, and offer a reliable confidence assessment capability. The detection and false alarm probabilities required for a hyperspectral sensor to be a useful intelligence asset varies based ...

Chlorinated hydrocarbons represent the most prevalent contaminants in the subsurface, threatening the quality of groundwater at aquifers. Existing technologies for monitoring these contaminants require expensive, labor-intensive methods of sample collection and analysis. The goal of this Phase I research project is to develop a low-cost, compact, reliable, automated, unattended, and long-term mo ...