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Multisensor Data Fusion Of Remotely Sensed Imagery For Crop Field Mapping
1P. Moulton, 2Y. Lan, 3H. Zhang, 4C. Yang, 2D. Martin, 3R. Lacey, 2Y. Huang, 2W. C. Hoffmann
1. Stellar Solutions Inc.
2. USDA ARS
3. Texas A&M University
4. USDA-ARS

 

A wide variety of remote sensing data from airborne hyperspectral and multispectral images is available for site-specific management in agricultural application and production. Aerial imaging system may offer less expensive and high spatial resolution imagery with Near Infra-Red, Red, Green and Blue spectral wavebands. Hyperspectral sensor provides hundreds of spectral bands. Multisensor data fusion provides an effective paradigm for remote sensing applications by synthesizing data from multiple sensors or sources. A high-resolution CIR aerial photo can be integrated with hyperspectral images to complement each other for the improved information extraction. Aerial photos can be processed by the state-of-the-art image segmentation algorithms to generate individual objects that often correspond to physically meaningful entities, e.g., a crop condition. Hyperspectral data, commonly with hundreds of spectral bands, can be analyzed using conventional approaches such as spectral indices or possibly more effectively, using advanced data mining models or machine learning algorithms such as support vector machines and Gaussian processes models for accurate retrieval of quantitative information.
Keyword: multisensor data fusion, images, airborne, crop condition
P. Moulton    Y. Lan    H. Zhang    C. Yang    D. Martin    R. Lacey    Y. Huang    W. C. Hoffmann    Remote Sensing Applications in Precision Agriculture    Poster    2010