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The Award database is continually updated throughout the year. As a result, data for FY19 is not expected to be complete until April, 2020.

  1. Low-Shot Detection in Remote Sensing Imagery

    SBC: Next Century Corporation            Topic: NGA172002

    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 ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  2. GFNet: Gnostic Fields based Low-Shot Learning for Target Detection in Remote Sensing

    SBC: Intelligent Automation, Inc.            Topic: NGA172002

    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 ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  3. DNC-GD: Deep Neural Network Compression for Geospatial Data

    SBC: Intelligent Automation, Inc.            Topic: NGA181009

    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 ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  4. TOFENetTopographic Features Extraction Network

    SBC: Intelligent Automation, Inc.            Topic: NGA181001

    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 ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  5. TopoRobo (Topographical Annotation Robot Software)

    SBC: Next Century Corporation            Topic: NGA181001

    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 ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  6. CloudVS: Next Generation Video Services In Cloud Computing

    SBC: Intelligent Automation, Inc.            Topic: NGA181002

    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 ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency

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