Auto-Label SAR (AL-SAR)
Award last edited on: 3/29/2023

Sponsored Program
Awarding Agency
Total Award Amount
Award Phase
Solicitation Topic Code
Principal Investigator
Nathan Heavner

Company Information


6200 South Main Street
Aurora, CO 80016
   (720) 728-7040
Location: Multiple
Congr. District: 06
County: Arapahoe

Phase I

Contract Number: HM047622C0061
Start Date: 9/15/2022    Completed: 6/14/2023
Phase I year
Phase I Amount
Correctly labeled data is essential for training AI/ML-based automatic target recognition (ATR). The training process is all the more complicated in synthetic aperture radar (SAR) images because of their unique phenomenology, such as orientation-sensitive target signatures, layover, cross-range smearing, and radio frequency interference. New automated technology must reduce the cost and accelerate the timeline of manual labeling. We propose Auto Label SAR (AL-SAR) by extending our DELPHI Active Learning (AL) framework for single-target SAR image classification to a framework for multi-target classification. In Phase I, we will leverage open-source Python and state-of-the-art AL with Core-set selection. Our Phase 1 feasibility study will simulate human-in-the-loop labeling using publicly available or government provided pre-labeled training data. Resources will be focused on critical technical questions and experiments to illuminate and prioritize key enabling capabilities for AL-SAR development during Phases II-III. AL-SAR will be an automated, cost-effective, and adaptable target labeling tool which could also be used to label targets in other mission areas with different data sources.

Phase II

Contract Number: ----------
Start Date: 00/00/00    Completed: 00/00/00
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