55 citations · 93 across the 6 of their papers we have counts for
7 papers
Learning to See More: UAS-Guided Super-Resolution of Satellite Imagery for Precision Agriculture
Arif Masrur, Peder A. Olsen, Paul R. Adler +4
Unmanned Aircraft Systems (UAS) and satellites are key data sources for precision agriculture, yet each presents trade-offs. Satellite data offer broad spatial, temporal, and spect…
Seeing Through Clouds in Satellite Images
Mingmin Zhao, Peder A. Olsen, Ranveer Chandra
This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images. We leverage radio frequency (RF) signals in the ultra/super-high frequ…
Counting and Segmenting Sorghum Heads
Min-hwan Oh, Peder Olsen, Karthikeyan Natesan Ramamurthy
Phenotyping is the process of measuring an organism's observable traits. Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and error prone process. Accurate…
Crowd Counting with Decomposed Uncertainty
Min-hwan Oh, Peder A. Olsen, Karthikeyan Natesan Ramamurthy
Research in neural networks in the field of computer vision has achieved remarkable accuracy for point estimation. However, the uncertainty in the estimation is rarely addressed. U…
Improving Simple Models with Confidence Profiles
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss +1
In this paper, we propose a new method called ProfWeight for transferring information from a pre-trained deep neural network that has a high test accuracy to a simpler interpretabl…
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
Jialei Wang, Peder A. Olsen, Andrew R. Conn +1
We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing. Our approach first detects and remo…