29 citations · 41 across the 15 of their papers we have counts for
14 papers · 1 filter
Improved Automatic Target Recognition in Synthetic Aperture Sonar Imagery Using Large Deep Neural Networks
C. J. Moore, Alex Hurt, Jordan Malof
Automatic Target Recognition (ATR) in Synthetic Aperture Sonar (SAS) is a task largely dominated by deep neural networks (DNNs). Most SAS-ATR models use convolutional neural networ…
Global Building Area Estimation Products: How Accurate Are They?
Saad Lahrichi, Doa'a Allabadi, Kyle Bradbury +1
Geo-spatial rasters of building footprint area are useful for a variety of tasks, such as monitoring urbanization, improving energy efficiency, and tracking greenhouse gas emission…
Is Self-Supervised Pre-training on Satellite Imagery Better than ImageNet? A Systematic Study with Sentinel-2
Saad Lahrichi, Zion Sheng, Shufan Xia +2
Self-supervised learning (SSL) has demonstrated significant potential in pre-training robust models with limited labeled data, making it particularly valuable for remote sensing (R…
Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?
Kaleb Kassaw, Francesco Luzi, Leslie M. Collins +1
Image classification models, including convolutional neural networks (CNNs), perform well on a variety of classification tasks but struggle under conditions of partial occlusion, i…
Segment anything, from space?
Simiao Ren, Francesco Luzi, Saad Lahrichi +4
Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input…
Transformers For Recognition In Overhead Imagery: A Reality Check
Francesco Luzi, Aneesh Gupta, Leslie Collins +2
There is evidence that transformers offer state-of-the-art recognition performance on tasks involving overhead imagery (e.g., satellite imagery). However, it is difficult to make u…