World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
40
Citations
8625
World Ranking
7199
National Ranking
1965

Research.com Recognitions

  • 2007 - IEEE Fellow For applications of satellite data and airborne LIDAR imagery

Overview

Melba M. Crawford is affiliated with Purdue University West Lafayette in the United States. Their research primarily focuses on environmental science and agricultural and biological sciences, with a notable emphasis on subfields such as ecology, plant science, environmental engineering, media technology, and agronomy and crop science.

The main topics covered in their work include remote sensing in agriculture, remote sensing and LiDAR applications, smart agriculture and artificial intelligence, leaf properties and growth measurement, remote-sensing image classification, crop yield and soil fertility, as well as spectroscopy and chemometric analyses.

Melba M. Crawford has published extensively, contributing to several scientific venues. The most frequent publication outlets include:

  • Remote Sensing
  • arXiv (Cornell University)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Frontiers in Plant Science
  • IEEE Transactions on Geoscience and Remote Sensing

The scientist has collaborated repeatedly with a group of coauthors, including:

  • Mitchell R. Tuinstra
  • Edward J. Delp
  • Ayman Habib
  • Saurabh Prasad
  • Wei Liu

Recent publications showcase the application of remote sensing and machine learning techniques in agriculture and environmental monitoring. Notable recent papers include:

  • "YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning," 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • "Automatic Plant Counting and Location Based on a Few-Shot Learning Technique," 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • "Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data," 2020, Remote Sensing
  • "Integrating crop growth models with remote sensing for predicting biomass yield of sorghum," 2021, in silico Plants
  • "New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping," 2021, Remote Sensing

In 2007, Melba M. Crawford was recognized as an IEEE Fellow for applications of satellite data and airborne LIDAR imagery, indicating a contribution to the use of advanced remote sensing technologies within their field of study.

Best Publications

  • Investigation of the random forest framework for classification of hyperspectral data

    J. Ham;Yangchi Chen;M.M. Crawford;J. Ghosh

  • Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification

    Li Ma;M M Crawford;Jinwen Tian

  • Feature Mining for Hyperspectral Image Classification

    Xiuping Jia;Bor-Chen Kuo;M. M. Crawford

  • Best-bases feature extraction algorithms for classification of hyperspectral data

    S. Kumar;J. Ghosh;M.M. Crawford

  • An Active Learning Approach to Hyperspectral Data Classification

    S. Rajan;J. Ghosh;M.M. Crawford

  • Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning

    Dalton Lunga;Saurabh Prasad;Melba M. Crawford;Okan Ersoy

  • Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis

    Shailesh Kumar;Joydeep Ghosh;Melba M. Crawford

  • Active Learning: Any Value for Classification of Remotely Sensed Data?

    M. M. Crawford;D. Tuia;H. L. Yang

  • Transfer Function Models of Daily Urban Water Use

    David R. Maidment;Shaw‐Pin ‐P Miaou;Melba M. Crawford

  • View Generation for Multiview Maximum Disagreement Based Active Learning for Hyperspectral Image Classification

    Wei Di;M. M. Crawford

  • Adaptive Classification for Hyperspectral Image Data Using Manifold Regularization Kernel Machines

    Wonkook Kim;Melba M Crawford

  • Local-Manifold-Learning-Based Graph Construction for Semisupervised Hyperspectral Image Classification

    Li Ma;Melba M. Crawford;Xiaoquan Yang;Yan Guo

  • Integrating support vector machines in a hierarchical output space decomposition framework

    Yangchi Chen;M.M. Crawford;J. Ghosh

  • Modeling and simulation of a nonhomogeneous poisson process having cyclic behavior

    Unknown

  • Active Learning via Multi-View and Local Proximity Co-Regularization for Hyperspectral Image Classification

    Wei Di;Melba M Crawford

  • Ensemble Multiple Kernel Active Learning For Classification of Multisource Remote Sensing Data

    Yuhang Zhang;Hsiuhan Lexie Yang;Saurabh Prasad;Edoardo Pasolli

  • Unsupervised multistage image classification using hierarchical clustering with a bayesian similarity measure

    Unknown

  • Anomaly Detection for Hyperspectral Images Based on Robust Locally Linear Embedding

    Li Ma;Li Ma;Melba M. Crawford;Jinwen Tian

  • An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

    Zhou Zhang;Edoardo Pasolli;Melba M. Crawford;James C. Tilton

  • Fusing interferometric radar and laser altimeter data to estimate surface topography and vegetation heights

    K.C. Slatton;M.M. Crawford;B.L. Evans

  • Applying nonlinear manifold learning to hyperspectral data for land cover classification

    Yangchi Chen;M.M. Crawford;J. Ghosh

  • Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery

    Ayman Habib;Youkyung Han;Weifeng Xiong;Fangning He

  • Improving Orthorectification of UAV-Based Push-Broom Scanner Imagery Using Derived Orthophotos From Frame Cameras

    Ayman Habib;Weifeng Xiong;Fangning He;Hsiuhan Lexie Yang

  • Comparative Analysis of HRU and Grid-Based SWAT Models

    Garett Pignotti;Hendrik Rathjens;Raj Cibin;Indrajeet Chaubey

  • Automatic Plant Counting and Location Based on a Few-Shot Learning Technique

    Azam Karami;Melba Crawford;Edward J. Delp

  • Boresight Calibration of GNSS/INS-Assisted Push-Broom Hyperspectral Scanners on UAV Platforms

    Ayman Habib;Tian Zhou;Ali Masjedi;Zhou Zhang

  • A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

    Jinha Jung;Edoardo Pasolli;Saurabh Prasad;James C. Tilton

  • A Hierarchical Multiclassifier System for Hyperspectral Data Analysis

    Shailesh Kumar;Joydeep Ghosh;Melba M. Crawford

  • Foreword to the Special Issue on Hyperspectral Image and Signal Processing

    Jocelyn Chanussot;Melba M. Crawford;Bor-Chen Kuo

Frequent Co-Authors

Saurabh Prasad
Saurabh Prasad University of Houston
Ayman Habib
Ayman Habib Purdue University West Lafayette
Edward J. Delp
Edward J. Delp Purdue University West Lafayette
Joydeep Ghosh
Joydeep Ghosh The University of Texas at Austin
Jinwen Tian
Jinwen Tian Huazhong University of Science and Technology
Mitchell R. Tuinstra
Mitchell R. Tuinstra Purdue University West Lafayette
Indrajeet Chaubey
Indrajeet Chaubey University of Connecticut
Scott C. Chapman
Scott C. Chapman University of Queensland
Graeme L. Hammer
Graeme L. Hammer University of Queensland
David S. Ebert
David S. Ebert University of Oklahoma

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