Multimodal Analysis of Drone based Imagery for Automatic Target Recognition
Abstract
This research proposes a large-scale dataset for training machine learning methods, a semantic segmentation network for visible and thermal images, and a novel annotation tool and data augmentation framework. We propose to perform semantic segmentation of the multi-modal imagery gathered from unmanned aerial vehicle platform, provided by our collaborators at US Army Research Laboratory. Our methodology makes use of both visible and longwave infrared imagery and provides per pixel semantic segmentation. Our proposed research will focus on i) novel annotation of training data, ii) novel transformations of the training data to be performed/used due to lack of large volumes of annotated data for our problem, and iii) robust and accurate semantic segmentation and matching of visible and LWIR imagery.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 17, 2020
- Source ID
- N000142012462
Entities
People
- Chandra Kambhamettu
Organizations
- Office of Naval Research
- United States Navy
- University of Delaware