Jordan M. Malof

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Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery pdf

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2018, ArXiv.

Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2016, 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA).

Using queuing models to aid design and guide research effort for multimodality buried target detection systems

Jordan M. Malof, Leslie M. Collins, 2016, SPIE Defense + Security.

Improving convolutional neural networks for buried target detection in ground penetrating radar using transfer learning via pretraining

Jordan M. Malof, Leslie M. Collins, John Bralich, 2017, Defense + Security.

The poor generalization of deep convolutional networks to aerial imagery from new geographic locations: an empirical study with solar array detection

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2017, 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

How much shape information is enough, or too much? Designing imaging descriptors for threat detection in ground penetrating radar data

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2018, Defense + Security.

A feature learning approach for classifying buried threats in forward looking ground penetrating radar data

Jordan M. Malof, Leslie M. Collins, Joseph A. Camilo, 2016, SPIE Defense + Security.

Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: preliminary results

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

Target signature localization in GPR data by jointly estimating and matching templates

Jordan M. Malof, Leslie M. Collins, Kenneth D. Morton, 2015, Defense + Security Symposium.

Three-dimensional features, based on beamforming at multiple depths, improves landmine detection with a forward-looking ground-penetrating radar

Jordan M. Malof, Leslie M. Collins, Joseph A. Camilo, 2017, 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR).

Improvements to the Histogram of Oriented Gradient (HOG) prescreener for buried threat detection in ground penetrating radar data

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2017, Defense + Security.

The effect of translational variance in training and testing images on supervised buried threat detection algorithms for ground penetrating radar

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2017, 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR).

Open Energy Infrastructure Data Through Automated Assessment Using Satellite Imagery

Jordan M. Malof, Leslie M. Collins, M. A. Jeuland, 2018 .

Full Collection: Distributed Solar Photovoltaic Array Location and Extent Data Set for Remote Sensing Object Identification

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2016 .

Reliable training of convolutional neural networks for GPR-based buried threat detection using the Adam optimizer and batch normalization

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2019, Defense + Commercial Sensing.

Leveraging seed dictionaries to improve dictionary learning

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2016, 2016 IEEE International Conference on Image Processing (ICIP).

The poor generalization of deep convolutional networks to aerial imagery from new geographic locations: an empirical study with solar array detection

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2017, 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

How do we choose the best model? The impact of cross-validation design on model evaluation for buried threat detection in ground penetrating radar

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2018, Defense + Security.

Data Augmentation in feature-space with Generative Adversarial Networks, applied to GPR-based Buried Threat Detection

Jordan M. Malof, Leslie M. Collins, Daniel Reichman, 2019 .

Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations pdf

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2018, 1805.12219.

A novel framework for processing forward looking infrared imagery with application to buried threat detection

Jordan M. Malof, Leslie M. Collins, Kenneth D. Morton, 2013, Defense, Security, and Sensing.

Change-based threat detection in urban environments with a forward-looking camera

Jordan M. Malof, Leslie M. Collins, Kenneth D. Morton, 2012, Other Conferences.

Estimating the electricity generation capacity of solar photovoltaic arrays using only color aerial imagery

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

Automatic solar photovoltaic panel detection in satellite imagery

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2015, 2015 International Conference on Renewable Energy Research and Applications (ICRERA).

On The Extraction of Training Imagery from Very Large Remote Sensing Datasets for Deep Convolutional Segmenatation Networks

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

Trading spatial resolution for improved accuracy when using detection algorithms on remote sensing imagery

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

Mapping solar array location, size, and capacity using deep learning and overhead imagery pdf

Jordan M. Malof, Kyle Bradbury, Bohao Huang, 2019, ArXiv.

Improving the histogram of oriented gradient feature for threat detection in ground penetrating radar by implementing it as a trainable convolutional neural network

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2018, Defense + Security.

Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

A comparative study of database reduction methods for case-based computer-aided detection systems: preliminary results

Jordan M. Malof, Jacek M. Zurada, Georgia D. Tourassi, 2009, Medical Imaging.

How robust are deep object detectors to variability in ground truth bounding boxes? Experiments for target recognition in infrared imagery

Jordan M. Malof, Leslie M. Collins, Francisco Reveriano, 2020, Defense + Commercial Sensing.

The effect of class imbalance on case selection for case-based classifiers, with emphasis on computer-aided diagnosis systems

Jordan M. Malof, Georgia D. Tourassi, Maciej A. Mazurowski, 2009, 2009 International Joint Conference on Neural Networks.

Discriminative dictionary learning to learn effective features for detecting buried threats in ground penetrating radar data

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2017, Defense + Security.

gprHOG and the popularity of Histogram of Oriented Gradients (HOG) for Buried Threat Detection in Ground-Penetrating Radar

Jordan M. Malof, Leslie M. Collins, Daniel Reichman, 2018 .

Automated Building Energy Consumption Estimation from Aerial Imagery

Jordan M. Malof, Kyle Bradbury, Artem Streltsov, 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

A deep convolutional neural network, with pre-training, for solar photovoltaic array detection in aerial imagery

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

Learning improved pooling regions for the Histogram of Oriented Gradient (HOG) feature for buried threat detection in ground penetrating radar

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2017, Defense + Security.

A simple rotational equivariance loss for generic convolutional segmentation networks: preliminary results

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

A novel algorithm for buried target detection evaluated on a collection of seismo-acoustic data

Jordan M. Malof, Leslie M. Collins, Kenneth D. Morton, 2014, Defense + Security Symposium.

Processing forward-looking data for anomaly detection: single-look, multi-look, and spatial classification

Jordan M. Malof, Leslie M. Collins, Kenneth D. Morton, 2012, Other Conferences.

A Probabilistic Model for Designing Multimodality Landmine Detection Systems to Improve Rates of Advance

Jordan M. Malof, Leslie M. Collins, Peter Torrione, 2016, IEEE Transactions on Geoscience and Remote Sensing.

Trading spatial resolution for improved accuracy in remote sensing imagery: an empirical study using synthetic data

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2017, 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

A queuing model for designing multi-modality buried target detection systems: preliminary results

Jordan M. Malof, Leslie M. Collins, Kenneth D. Morton, 2015, Defense + Security Symposium.

The effect of class imbalance on case selection for case-based classifiers: An empirical study in the context of medical decision support

Jordan M. Malof, Georgia D. Tourassi, Maciej A. Mazurowski, 2012, Neural Networks.

gprHOG: Several Simple Improvements to the Histogram of Oriented Gradients Feature for Threat Detection in Ground-Penetrating Radar pdf

Jordan M. Malof, Leslie M. Collins, Daniel Reichman, 2018, ArXiv.

Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2017, 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR).

Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery pdf

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2016, ArXiv.

A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar

Jordan M. Malof, Leslie M. Collins, Joseph N. Wilson, 2018, IEEE Transactions on Geoscience and Remote Sensing.

Clutter and target discrimination in forward-looking ground penetrating radar using sparse structured basis pursuits

Jordan M. Malof, Leslie M. Collins, Kenneth D. Morton, 2015, Defense + Security Symposium.

The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation

Jordan M. Malof, Kyle Bradbury, Fanjie Kong, 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

Algorithm development for deeply buried threat detection in GPR data

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2016, SPIE Defense + Security.

Statistical Models for Improving the Rate of Advance of Buried Target Detection Systems

Jordan M. Malof, 2015 .

An improved frequency domain feature with partial least-squares dimensionality reduction for classifying buried threats in forward-looking ground-penetrating radar data

Jordan M. Malof, Kenneth D. Morton, Leslie M. Collins, 2017, Defense + Security.

Deep Convolutional Segmentation of Remote Sensing Imagery: A Simple and Efficient Alternative to Stitching Output Labels

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

A Large Comparison of Feature-Based Approaches for Buried Target Classification in Forward-Looking Ground-Penetrating Radar

Jordan M. Malof, Leslie M. Collins, Joseph A. Camilo, 2017, IEEE Transactions on Geoscience and Remote Sensing.

Estimating residential building energy consumption using overhead imagery

Jordan M. Malof, Kyle Bradbury, Bohao Huang, 2020 .

Fusion of forward looking infrared and ground penetrating radar for improved stopping distances in landmine detection

Jordan M. Malof, Leslie M. Collins, Kenneth D. Morton, 2014, Defense + Security Symposium.

Semisupervised Adversarial Discriminative Domain Adaptation, with Applicationto Remote Sensing Data

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

On Choosing Training and Testing Data for Supervised Algorithms in Ground-Penetrating Radar Data for Buried Threat Detection

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2016, IEEE Transactions on Geoscience and Remote Sensing.

If training data appears to be mislabeled, should we relabel it? Improving supervised learning algorithms for threat detection in ground penetrating radar data

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2018, Defense + Security.

How transferable are downward-looking and handheld ground penetrating radar data? Experiments in the context of buried threat detection

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2019, Defense + Commercial Sensing.

An exploration of gradient-based features for buried threat detection using a handheld ground penetrating radar

Jordan M. Malof, Leslie M. Collins, Daniël Reichman, 2019, Defense + Commercial Sensing.

Application of Compositional Neural Networks for Robust Classification of Infrared Imagery

Jordan M. Malof, Leslie M. Collins, Gregory P. Spell, 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery pdf

Jordan M. Malof, Kyle Bradbury, Bohao Huang, 2021, ArXiv.

SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems pdf

Jordan M. Malof, Kyle Bradbury, Bohao Huang, 2021, ArXiv.

Inverse deep learning methods and benchmarks for artificial electromagnetic material design pdf

Jordan M. Malof, Yang Deng, Simiao Ren, 2021, Nanoscale.

Wind Turbine Detection with Synthetic Overhead Imagery

Jordan M. Malof, Kyle Bradbury, Natalie Tarn, 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

GridTracer: Automatic Mapping of Power Grids Using Deep Learning and Overhead Imagery pdf

Jordan M. Malof, Leslie M. Collins, Kyle Bradbury, 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Deep learning for accelerated all-dielectric metasurface design.

Jordan M. Malof, Willie J. Padilla, Bohao Huang, 2019, Optics express.

Optimizing drug therapy with Reinforcement Learning: The case of Anemia Management

Jordan M. Malof, Adam E. Gaweda, A. Gaweda, 2011, The 2011 International Joint Conference on Neural Networks.

Neural-adjoint method for the inverse design of all-dielectric metasurfaces.

Jordan M. Malof, Willie J Padilla, K. Fan, 2020, Optics express.

Imaging descriptors improve the predictive power of survival models for glioblastoma patients.

Jordan M. Malof, M. Mazurowski, A. Desjardins, 2013, Neuro-oncology.

Toward VO-based Collaboration between Computational Intelligence-Machine Learning and Healthcare Communities

Jordan M. Malof, Jacek M. Zurada, Janusz Wojtusiak, 2009 .

Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning

Jordan M. Malof, Kyle Bradbury, Simiao Ren, 2022, ISPRS Int. J. Geo Inf..

Learning the Physics of All‐Dielectric Metamaterials with Deep Lorentz Neural Networks

Jordan M. Malof, Willie J. Padilla, Simiao Ren, 2022, Advanced Optical Materials.

Transformers For Recognition In Overhead Imagery: A Reality Check

Jordan M. Malof, Kyle Bradbury, L. Collins, 2022, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Meta-simulation for the Automated Design of Synthetic Overhead Imagery

Jordan M. Malof, L. Collins, Handi Yu, 2022, ArXiv.

Towards Robust Deep Active Learning for Scientific Computing

Jordan M. Malof, Willie J. Padilla, Yang Deng, 2022, 2201.12632.

Dense labeling of large remote sensing imagery with convolutional neural networks: a simple and faster alternative to stitching output label maps

Jordan M. Malof, Daniel Reichman, Bohao Huang, 2018, ArXiv.

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Jordan M. Malof, Willie J Padilla, Simiao Ren, 2021, Advanced Functional Materials.

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

Jordan M. Malof, Kyle Bradbury, Wuming Zhang, 2016, Scientific Data.

Benchmarking deep inverse models over time, and the neural-adjoint method pdf

Jordan M. Malof, Simiao Ren, Willie Padilla, 2020, NeurIPS.

Deep Active Learning for Scientific Computing in the Wild

Jordan M. Malof, Willie J. Padilla, Yang Deng, 2023, ArXiv.

Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials

Jordan M. Malof, Willie J. Padilla, V. Tarokh, 2021, NeurIPS Datasets and Benchmarks.

A deep convolutional neural network and a random forest classifier for solar photovoltaic array detection in aerial imagery

Jordan M. Malof, R. Newell, Kyle Bradbury, 2016, 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA).

Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer

Jordan M. Malof, L. Collins, Francesco Luzi, 2022, ArXiv.

Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support

Jordan M. Malof, Jordan M Malof, Maciej A Mazurowski, 2011, Physics in medicine and biology.

Soft-masks guided faster region-based convolutional neural network for domain adaptation in wind turbine detection

Jordan M. Malof, Bohao Huang, Manman Yuan, 2023, Frontiers in Energy Research.

SIMPL: Generating Synthetic Overhead Imagery to Address Custom Zero-Shot and Few-Shot Detection Problems

Jordan M. Malof, Bohao Huang, Kyle Bradbury, 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation

Jordan M. Malof, Bohao Huang, Kyle Bradbury, 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

Deep Generalized Green's Functions

Jordan M. Malof, Willie J. Padilla, V. Tarokh, 2023, ArXiv.

Hyperparameter-free deep active learning for regression problems via query synthesis

Jordan M. Malof, Willie J. Padilla, Yang Deng, 2022, ArXiv.

Deep Inverse Photonic Design: A Tutorial

Jordan M. Malof, Willie J. Padilla, Yang Deng, 2022, SSRN Electronic Journal.

Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

Jordan M. Malof, L. M. Valeri, Kyle Bradbury, 2022, Applied Energy.

What you get is not always what you see—pitfalls in solar array assessment using overhead imagery

Jordan M. Malof, Bohao Huang, Kyle Bradbury, 2019, Applied Energy.

Self-Supervised Encoders Are Better Transfer Learners in Remote Sensing Applications

Jordan M. Malof, Kyle Bradbury, Simiao Ren, 2022, Remote Sensing.

Segment anything, from space?

Jordan M. Malof, Kyle Bradbury, L. Collins, 2023, ArXiv.

Machine Learning for Mie-Tronics

Jordan M. Malof, Willie J. Padilla, N. Litchinitser, 2023, 2305.18589.

Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling

Jordan M. Malof, Gregory P. Spell, L. Collins, 2022, AAAI.