Z. Zhang, E. Pasolli, and M. Crawford, “An Adaptive Multiview Active Learning Approach for Spectral-Spatial Classification of Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, 58(4), 2557-2570, 2020.
A. Masjedi, N. Carpenter, M. Crawford, and M. Tuinstra, “Prediction of Sorghum Biomass Using UAV Time Series Data and Recurrent Neural Networks,” CVPR 2019 Computer Vision Problems in Plant Phenotyping (CVPPP 2019), June 17, 2019, San Diego, CA.
J. Chi and M. Crawford, A New Global Arctic Sea Ice Concentration Retrieval Algorithm by Incorporating Spectral Mixture Analysis and Deep Learning,” Remote Sensing of Environment, 231, Article 111204, 2019.
G. Taskin, M. Crawford, “An Out-of-Sample Extension to Manifold Learning via Meta-Modeling,” IEEE Transactions on Image Processing, 28(10), 5227-5238, 2019.
L. Ma, L. Zhu, M. Crawford, and Y. Liu “Centroid and Covariance Alignment based Domain Adaptation for Unsupervised Classification of Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, 57(4), 2305-2323, 2019.
A. Habib, T. Zhou, A. Masjedi, Z. Zhou, J.E. Flatt, M. Crawford, “Boresight Calibration of GNSS/INS-assisted Push-broom Hyperspectral scanners on UAV platforms,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS), 11(5), 1734-1739, 2018.
G. Pignotti, H. Rathjens, R. Cibin, I. Chaubey, and M. Crawford, “Comparative Analysis of Spatial Resolution Effects on HRU and Grid-based SWAT Models.” Water, 9(4), 272, 1-20, 2017.
Z. Zhang and M. Crawford, “Batch-mode Regularized Multi-metric Active Learning for Classification of Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6594-6609, 2017.
E. Ibrahim, W. Kim, M. Crawford, and J. Monbaliu, “A Regression Approach to the Mapping of Bio-physical Characteristics of Surface Sediment using In situ and Airborne Hyperspectral Acquisitions,” Ocean Dynamics, 67, 299–316, 2017.
Z. Zhang, E. Pasolli, H.H. Yang, and M. Crawford “Multi-metric Active Learning for Classification of Multisource Remotely Sensed Data,” IEEE Geoscience and Remote Sensing Letters, 13(7), 1007-1011, 2016.
Z. Zhang, E. Pasolli, M. Crawford, and J. Tilton, “An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation, J. Special Topics in Applied Earth Observations and Remote Sensing, 9(2), 640-654, 2016.
X. Zhou; S. Prasad, and M. Crawford, “Wavelet-Domain Multiview Active Learning for Spatial-spectral Hyperspectral Image Classification, IEEE J. Special Topics in Applied Earth Observations and Remote Sensing, 9(9), 4047-4059, 2016.
H. L. Yang and M. Crawford, “Domain Adaptation with Preservation of Manifold Geometry for Hyperspectral Image Classification,” IEEE J. Special Topics in Applied Earth Observations and Remote Sensing, 9(2), 543-555, 2016.
H. L. Yang and M. Crawford, “Spectral and Spatial Proximity Based Manifold Alignment for Multitemporal Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, 54(1), 51-64, 2016.
L. Ma, M. Crawford, X. Yang, and Y. Guo, “Local Manifold-Learning-Based Graph Construction for Semi-Supervised Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2832-2844, 2015.
Y. Zhang, H.H. Yang, S. Prasad, E. Pasolli, J. Jung, and M. Crawford, “Ensemble Multiple Kernel Active Learning for Classification of Multi-Source Remote Sensing Data,” J. Special Topics in Applied Earth Observations and Remote Sensing, 8(2), 845-858, 2015.
J. Jung, E. Pasolli, S. Prasad, J. Tilton, and M. Crawford, “A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation,” J. Special Topics in Applied Earth Observations and Remote Sensing, 7(2), 491-502, 2014.
J. Chi and M. Crawford, “Spectral Unmixing Based Crop Residue Estimation,” J. Special Topics in Applied Earth Observations and Remote Sensing, 7(6), 2531- 2539, 2014.
J. Chi and M. Crawfordd, “Active Landmark Sampling for Manifold Learning Based Spectral Unmixing,” IEEE Geoscience and Remote Sensing Letters, 11(11), 1881–1885, 2014.
Lunga, D., S. Prasad, M. Crawford, and O. Ersoy, “Manifold Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning,” IEEE Signal Processing Magazine, 31(1), 55-66, 2014.
J. Chi and M. Crawford, “Selection of Landmark Points on Nonlinear Manifolds for Spectral Unmixing Using Local Homogeneity,” IEEE Geoscience and Remote Sensing Letters, 10(4), 711-715, 2013.
M. Galloza, M. Crawford, and G. Heathman, “Crop Residue Modeling and Mapping Using Landsat, ALI, Hyperion, and Airborne Remote Sensing Data,” J. Special Topics in Applied Earth Observations and Remote Sensing, 6(2), 446-456, 2013.
M. Crawford, D. Tuia, and H. Yang, “Active Learning: Any Value for Remote Sensing Applications?” Proceedings of the IEEE, 101(3), 593-608, 2013.
X. Jia, B. Kuo, M.M. Crawford, “Feature Mining for Hyperspectral Image Classification,” Proceedings of the IEEE, 101(3), 676-697, 2013.
W. Di and M. Crawford, “View Generation for Multi-view Maximum Disagreement Based Active Learning for Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1942-1954, 2012.
J. Jung and M. Crawford, “Extraction of Features from LIDAR Waveform Data for Characterizing Forest Structure,” IEEE Geoscience and Remote Sensing Letters, 9(3), 492-496, 2012.