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The Award database is continually updated throughout the year. As a result, data for FY21 is not expected to be complete until September, 2022.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

  1. Low-Shot Detection in Remote Sensing Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA172002

    Toyon Research Corporation proposes to develop algorithms that improve the precision and recall of neural networks for low-shot object classification and detection. Our approach is based upon developing a descriptive multi-domain feature representation of the low-shot target as well as the surrounding context. A multi-branch neural network merges the various domains of information to perform accur ...

    SBIR Phase II 2019 Department of DefenseNational Geospatial-Intelligence Agency
  2. Generalized Change Detection to Cue Regions of Interest

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181006

    Toyon proposes to research and develop algorithms for generalized salient change detection, and to incorporate these algorithms into software tools implemented on the cloud. Our approach leverages the two most promising methods from Phase I, both based on supervised learning. The first method is the entropy-based feature vector and corresponding neural network, which we will apply at a coarse sear ...

    SBIR Phase II 2019 Department of DefenseNational Geospatial-Intelligence Agency
  3. Advanced Image Segmentation for Radar Imagery

    SBC: ELECTROMAGNETIC SYSTEMS, INC.            Topic: NGA172004

    Implement methods to improve our Phase I architecture for complex SAR imagery collected from different systems and with different collection parameters. Implement techniques to boost performance across all ground features of interest. Reduce the amount of human intervention needed for training our SAR image segmentation architecture.  SAR segmentation can be applied to a number of areas: Intellig ...

    SBIR Phase II 2019 Department of DefenseNational Geospatial-Intelligence Agency
  4. Low-Shot Detection in Remote Sensing Imagery

    SBC: Novateur Research Solutions, LLC            Topic: NGA172002

    This SBIR Phase II project will develop biologically inspired computational models and algorithms to enable low-shot and one-shot detectionof objects-of-interest in remote sensing imagery. The Phase II effort will build upon our Phase I work including multi-scale representationlearning framework and deep-learning based feature extraction and matching techniques for low-shot target detection. The P ...

    SBIR Phase II 2018 Department of DefenseNational Geospatial-Intelligence Agency
  5. Improved Image Processing for Low Resolution Imagery with Inter-Frame Pose Variation

    SBC: Etegent Technologies, Ltd.            Topic: NGA172009

    With respect to digital imaging sensors and systems, obtaining as many pixels on target is a necessity to classify or identify a target for real-time operations and forensic analysis within the intelligence community. Rather than relying on improved sensors, the approach originally solicited and further refined here utilizes super-resolution image processing techniques to provide more detail in th ...

    SBIR Phase II 2019 Department of DefenseNational Geospatial-Intelligence Agency
  6. Video to Feature Data Association and

    SBC: Novateur Research Solutions, LLC            Topic: NGA181007

    This SBIR Phase II project proposes a probabilistic approach to determine a vehicle’s location using onboard video and Lidar sensors and foundation map data in GPS denied environments. The proposed system does not rely on only one type of information source, instead it combines proposals from a variety of location estimators to find a vehicles location in GPS-denied environments. The system take ...

    SBIR Phase II 2019 Department of DefenseNational Geospatial-Intelligence Agency
  7. Low-Shot Detection in Remote Sensing Imagery

    SBC: Etegent Technologies, Ltd.            Topic: NGA172002

    With the ever-growing number of imaging satellites in orbit the job of an analyst will change from eyes on pixels to analysis of informationfrom imagery thanks to automated image processing like object detection and change detection. Object detection algorithms have advancedto near human performance given that there is sufficient labeled data on which to train; however, obtaining this data is cost ...

    SBIR Phase II 2018 Department of DefenseNational Geospatial-Intelligence Agency
  8. 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
  9. Low-Shot Detection in Remote Sensing Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181010

    The National Geospatial-Intelligence Agency (NGA) ingests and analyzes raw imagery from multiple sources to form actionable intelligenceproducts that can be disseminated across the intelligence community (IC). To effectively meet these demands NGA must continue to improveits automated and semi-automated methods for target detection and classification. Of particular concern is furthering NGA's abil ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. Automated Assessment of Urban Environment Degradation for Disaster Relief andReconstruction

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181004

    Toyon Research Corp. proposes development of a system that automates disaster assessment based on fusion of overhead and ground-basedimages, video, and other data. In Phase I, we will investigate various possible data sources and the benefits of fusing the data in automatedanalysis. We will select and curate data for processing in a Phase I feasibility study. Damage assessment will be performed in ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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