Jordan M. Malof
Orcid: 0000-0002-7851-4920
According to our database1,
Jordan M. Malof
authored at least 53 papers
between 2009 and 2024.
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Bibliography
2024
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024
2023
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023
Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
2022
SIMPL: Generating Synthetic Overhead Imagery to Address Custom Zero-Shot and Few-Shot Detection Problems.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022
GridTracer: Automatic Mapping of Power Grids Using Deep Learning and Overhead Imagery.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022
Self-Supervised Encoders Are Better Transfer Learners in Remote Sensing Applications.
Remote. Sens., 2022
Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning.
ISPRS Int. J. Geo Inf., 2022
Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis.
CoRR, 2022
Hyperparameter-free deep active learning for regression problems via query synthesis.
CoRR, 2022
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions.
Proceedings of the Tenth International Conference on Learning Representations, 2022
2021
Inverse deep learning methods and benchmarks for artificial electromagnetic material design.
CoRR, 2021
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems.
CoRR, 2021
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery.
CoRR, 2021
Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021
Application of Compositional Neural Networks for Robust Classification of Infrared Imagery.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021
2020
The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Designing Synthetic Overhead Imagery to Match a Target Geographic Region: Preliminary Results Training Deep Learning Models.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020
Do Deep Learning Models Generalize to Overhead Imagery from Novel Geographic Domains? The xGD Benchmark Problem.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020
Mapping Electric Transmission Line Infrastructure from Aerial Imagery with Deep Learning.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020
2019
A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar.
IEEE Trans. Geosci. Remote. Sens., 2019
Mapping solar array location, size, and capacity using deep learning and overhead imagery.
CoRR, 2019
A simple rotational equivariance loss for generic convolutional segmentation networks: preliminary results.
Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: preliminary results.
Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
2018
On Choosing Training and Testing Data for Supervised Algorithms in Ground-Penetrating Radar Data for Buried Threat Detection.
IEEE Trans. Geosci. Remote. Sens., 2018
A Large Comparison of Feature-Based Approaches for Buried Target Classification in Forward-Looking Ground-Penetrating Radar.
IEEE Trans. Geosci. Remote. Sens., 2018
gprHOG: Several Simple Improvements to the Histogram of Oriented Gradients Feature for Threat Detection in Ground-Penetrating Radar.
CoRR, 2018
Dense labeling of large remote sensing imagery with convolutional neural networks: a simple and faster alternative to stitching output label maps.
CoRR, 2018
Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery.
CoRR, 2018
Semisupervised Adversarial Discriminative Domain Adaptation, with Applicationto Remote Sensing Data.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Deep Convolutional Segmentation of Remote Sensing Imagery: A Simple and Efficient Alternative to Stitching Output Labels.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
On The Extraction of Training Imagery from Very Large Remote Sensing Datasets for Deep Convolutional Segmenatation Networks.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
2017
Estimating the electricity generation capacity of solar photovoltaic arrays using only color aerial imagery.
Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, 2017
Trading spatial resolution for improved accuracy when using detection algorithms on remote sensing imagery.
Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, 2017
A deep convolutional neural network, with pre-training, for solar photovoltaic array detection in aerial imagery.
Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, 2017
The poor generalization of deep convolutional networks to aerial imagery from new geographic locations: an empirical study with solar array detection.
Proceedings of the 2017 IEEE Applied Imagery Pattern Recognition Workshop, 2017
Trading spatial resolution for improved accuracy in remote sensing imagery: an empirical study using synthetic data.
Proceedings of the 2017 IEEE Applied Imagery Pattern Recognition Workshop, 2017
2016
A Probabilistic Model for Designing Multimodality Landmine Detection Systems to Improve Rates of Advance.
IEEE Trans. Geosci. Remote. Sens., 2016
CoRR, 2016
Proceedings of the 2016 IEEE International Conference on Image Processing, 2016
2015
Statistical Models for Improving the Rate of Advance of Buried Target Detection Systems.
PhD thesis, 2015
2012
The effect of class imbalance on case selection for case-based classifiers: An empirical study in the context of medical decision support.
Neural Networks, 2012
2011
Proceedings of the 2011 International Joint Conference on Neural Networks, 2011
2009
A comparative study of database reduction methods for case-based computer-aided detection systems: preliminary results.
Proceedings of the Medical Imaging 2009: Computer-Aided Diagnosis, 2009
The effect of class imbalance on case selection for case-based classifiers, with emphasis on computer-aided diagnosis systems.
Proceedings of the International Joint Conference on Neural Networks, 2009