Search Results for author:
Found 15 papers, 0 papers with code
Segment anything, from space?
In this work, we examine whether SAM's performance extends to overhead imagery problems and help guide the community's response to its development.
Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer
To address this problem, we explore cross-modal style transfer (CMST) to leverage large and diverse color imagery datasets so that they can be used to train DNN-based IR image based object detectors.
Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling
We propose and show the efficacy of a new method to address generic inverse problems.
Meta-simulation for the Automated Design of Synthetic Overhead Imagery
The use of synthetic (or simulated) data for training machine learning models has grown rapidly in recent years.
Phoneme-Based Ratio Mask Estimation for Reverberant Speech Enhancement in Cochlear Implant Processors
This study proposes a phoneme-based mask estimation algorithm, where separate mask estimation models are trained for each phoneme.
Assessing the intelligibility of vocoded speech using a remote testing framework
Over the past year, remote speech intelligibility testing has become a popular and necessary alternative to traditional in-person experiments due to the need for physical distancing during the COVID-19 pandemic.
Evaluating the Effect of Longitudinal Dose and INR Data on Maintenance Warfarin Dose Predictions
Since a sequence of prior doses and INR better capture the variability in individual warfarin response, we hypothesized that longitudinal dose response data will improve maintenance dose predictions.
GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery
Energy system information valuable for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers, termed the power grid, is often incomplete, outdated, or altogether unavailable.
gprHOG and the popularity of Histogram of Oriented Gradients (HOG) for Buried Threat Detection in Ground-Penetrating Radar
Substantial research has been devoted to the development of algorithms that automate buried threat detection (BTD) with ground penetrating radar (GPR) data, resulting in a large number of proposed algorithms.
Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations
In this work we consider the application of convolutional neural networks (CNNs) for pixel-wise labeling (a. k. a., semantic segmentation) of remote sensing imagery (e. g., aerial color or hyperspectral imagery).
Segmentation Of Remote Sensing Imagery
Semantic Segmentation
A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar
In this work we report the results of a multi-institutional effort to develop advanced buried threat detection algorithms for a real-world GPR BTD system.
Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery
In this work, we employ a state-of-the-art convolutional neural network architecture, called SegNet (Badrinarayanan et.
A large comparison of feature-based approaches for buried target classification in forward-looking ground-penetrating radar
The first goal of this work is to provide a comprehensive comparison of detection performance using existing features on a large collection of FLGPR data.
On Choosing Training and Testing Data for Supervised Algorithms in Ground Penetrating Radar Data for Buried Threat Detection
Training data most often consists of 2-dimensional images (or patches) of GPR data, from which features are extracted, and provided to the classifier during training and testing.
Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery
Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution.
