Award Data

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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 June, 2020.

  1. PCS-Enabled Unattended Radiation Detection and Attribution System

    SBC: Physical Sciences Inc.            Topic: HSB0171009

    Physical Sciences Inc. (PSI) proposes to develop a PCS-Enabled Unattended Radiation Detection and Attribution System (PURDAS) that will be able to detect, identify, and attribute radiological sources to specific source carriers or conveyances. The PURDAS will include a COTS gamma and neutron detection capability as well as a visible camera, onboard processing, and wireless radios. PURDAS units wil ...

    SBIR Phase II 2018 Department of Homeland SecurityDomestic Nuclear Detection Office SBIR Program
  2. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  3. Low-Shot Detection in Remote Sensing Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA172002

    Toyon Research Corporation proposes to research and develop algorithms for low-shot object detection, adapting popular techniques to address the complexities inherent in ATR for remote sensing. Traditional object detection algorithms rely on large corpora of data which may not be available for more exotic targets (such as foreign military assets), and therefore, traditional Convolutional Neural Ne ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  4. 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
  5. 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
  6. GEOFF: Geo-location from Edges, Objects, Foundational data, and a Filter

    SBC: Scientific Systems Company Inc.            Topic: NGA181007

    Ground vehicles with navigation capability (e.g., GPS) can index into foundation data (e.g., Google Maps) to gain situational awareness abouttheir surroundings. When GPS and RF navigation sources are degraded, maintaining situational awareness requires an alternative navigationsource. One alternative source is the foundation data itself. The data contain objects at known 3D locations, which projec ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  7. Variational Object Recognition and Grouping Network

    SBC: Intellisense Systems, Inc.            Topic: NGA181005

    To address the National Geospatial-Intelligence Agency (NGA) need for overhead imagery analysis algorithms that provide uncertaintymeasures for object recognition and aggregation, Intellisense Systems, Inc. (ISS) proposes to develop a new Variational Object Recognition andGrouping Network (VORGNet) system. It is based on the innovation of implementing a Bayesian convolutional neural network (CNN) ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  8. 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
  9. 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
  10. Bayesian Urban Degradation Assessment

    SBC: Intellisense Systems, Inc.            Topic: NGA181004

    To address the NGA need for algorithms that fuse observables from over-flight operations and from ground sources to automatically estimatethe degradation of urban environments due to battle damage or natural disasters, Intellisense Systems, Inc. (ISS) proposes to develop a newBayesian Urban Degradation Assessment (BUDA) software system. It is based on the integration of multiple damage assessment ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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