Melba Crawford

Melba Crawford

Associate Dean of Engineering for Research

Professor of Agronomy, Civil Engineering, and

Electrical & Computer Engineering

Phone: 765.496.9355

Fax: 765.496.2926

Office: Lilly 3-353

Area of Expertise: Methods in statistical pattern recognition for high dimensional data analysis, data fusion techniques for multisensor problems, multiresolution methods in image analysis, and knowledge transfer in data mining.

Personal Web Page

Curriculum Vitae

Professional Experience

Purdue University, Professor, 2006 - present

Associate Dean of Engineering for Research, 2010 - 2019

Interim Associate Dean of Engineering for Research, 2009 - 2010

Assistant Dean for Interdisciplinary Research in Agriculture and Engineering, 2006 - 2010

Director, Laboratory for Applications of Remote Sensing, 2006 – present

Interim Head of Civil Engineering, 2012

The University of Texas at Austin, Professor, 1991 - 2005

The University of Texas at Austin, Associate Professor, 1986 -1991

The University of Texas at Austin, Assistant Professor, 1980 -1986

The University of Texas at Dallas, Instructor, 1977-1980

Education

Ph.D., Ohio State University, 1981

MSCE, University of Illinois, 1973

BSCE, University of Illinois, 1970

Honors and Awards

UT Engineering Foundation Faculty Excellence Award, 1987, 1990

UT Engineering Foundation Faculty Fellowship #3, 1988 -1991

General Dynamics Departmental Teaching Excellence Award, 1990

Halliburton Education Foundation Award for Excellence in Teaching, 1991

Outstanding Graduate Faculty Award, Graduate School, University of Texas, 1993

Faculty Research Assignment, 1997 (U. of Arizona and U. of Wollongong)

Charlotte Maer Patton Centennial Fellow in Engineering, 1991-1998

Engineering Foundation Endowed Professorship #1, 1999 - 2005

NASA Outstanding Service Award, EO-1 Science Team, 2002

Outstanding Paper Award, IICAI-2003, Hyderabad, India, Dec. 18 - 20, 2003

Jefferson Senior Science Fellow, U.S. State Department, 2004-2005

Meritorious Honor Award, Delegation to World Conference on Disaster Reduction, U.S. State Department, 2005

Purdue Chair of Excellence in Earth Observation, 2006 - present

Fellow, Institute for Electrical and Electronics Engineers, 2007- Present

Fellow, Academic Leadership Program, Committee on Institutional Cooperation, 2007- 2008

Woman of Purdue, Mortar Board Honoree, 2009

Mortar Board, Honorary Member, 2009 - 2010

Purdue Provost Fellow for Globalization Initiatives, 2009

Purdue College of Engineering Team Award for SURF program, 2012

Nancy Uridil and Francis Bossu Professor in Civil Engineering

IEEE GRSS Outstanding Service Award, 2020

Other Appointments

Civil Engineering and Director of Laboratory for Applications of Remote Sensing (LARS)

Specialty Group: Geomatics Engineering

Recent Publications

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.

W. Di and M. Crawford, “Active Learning via Multi-View and Local Proximity Co-regularization for Hyperspectral Image Classification,” IEEE Journal of Selected Topics in Signal Processing, 5(3), 618-628, 2011.

J. Jung and M. Crawford, “Decomposition of Waveform LIDAR Data for Terrestrial Applications,” IEEE Geoscience and Remote Sensing Letters, in review.

B. Naz, L. Bowling, and M. Crawford, “Spatial and Temporal Glacier Changes in the Central Karakoram Himalaya Derived from Landsat Satellite and Climate Data,” Journal of Glaciology, in review.

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, in review.

B. Naz, L. Bowling,  M. Crawford, “Quantification of Glacier Changes Using ICESat Elevation Data and the SRTM Digital Elevation Model in the Upper Indus Basin,” Journal of Glaciology, in review.

W. Di and  M. Crawford, “Critical Class Oriented Active Learning for Hyperspectral Image Classification,” 2011 IEEE Geoscience and Remote Sensing Symposium, July 25-29, Vancouver, BC, 2011, accepted.

M. Galloza and  M. Crawford, “Exploiting Multisensor Spectral Data to Estimate Crop Residue Cover for Management of Agricultural Water Quality,” 2011 IEEE Geoscience and Remote Sensing Symposium, July 25-29, Vancouver, BC, 2011, accepted.

Recent Book Chapter
E. Pasolli, S. Prasad, M. Crawford, and J. Tilton, “Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies,” S. Prasad, J. Lyon, A. Huete (eds). CRC Press, 2018.

M. Crawford, Editor, “Remote Sensing Data Processing and Analysis Methodology,” vol. 2, Remote Sensing Data Processing and Analysis Methodology, Ed. Shunlin Liang, 2017.

M. Crawford, L. Ma, W. Kim, “Exploring Nonlinear Manifold Learning for Classification of Hyperspectral Data,” in Optical Remote Sensing: Advances in Signal Processing and Exploitation Techniques, S. Prasad, J. Chanussot, L. Bruce (Eds), Springer Verlag, London, 2011.

L. Ma and M.M. Crawford, “Local Manifold Learning Based K-Nearest Neighbor for Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, 48(11), 4099-4109, 2010.

W. Kim and M.M. Crawford, “Adaptive Classification of Hyperspectral Image Data using Manifold Regularization Kernel Machines,” IEEE Trans. on Geoscience and Remote Sensing, 48(11), 4110-4121, 2010.

W. Kim, M.M. Crawford, and S. Lee, “Integrating Spatial Proximity with Manifold Learning for Hyperspectral Data, Korean Journal of Remote Sensing 26(6), 693-703, 2010.

Date joined Staff: 2006