Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area |
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| Institution: | 1. Department of Economic Geology, School of Geology, University of Tehran, Tehran, Iran;2. Department of Earth Sciences, Faculty of Sciences, Shiraz University, Shiraz, Iran;1. School of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-tech Zone, Chengdu, Sichuan 611731, China;2. Center for Information Geoscience, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-tech Zone, Chengdu, Sichuan 611731, China;3. Department of Geography, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium;1. Department of Petrology and Economic Geology, Geological Survey of Denmark and Greenland, Copenhagen, Denmark;2. Department of Geosciences and Natural Resource Management, University Of Copenhagen, Copenhagen, Denmark;3. German Remote Sensing Data Centre, DLR, Munchnerstr. 20, D-82234, Germany;4. Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton T6G 2E3, Canada;1. Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106-4060, United States;2. National Aeronautics and Space Administration Jet Propulsion Laboratory, Pasadena, CA 91109, United States;3. University of Florida, Gainesville, FL 32611, United States |
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| Abstract: | Spectral feature fitting (SFF) is a commonly used strategy for hyperspectral imagery analysis to discriminate ground targets. Compared to other image analysis techniques, SFF does not secure higher accuracy in extracting image information in all circumstances. Multi range spectral feature fitting (MRSFF) from ENVI software allows user to focus on those interesting spectral features to yield better performance. Thus spectral wavelength ranges and their corresponding weights must be determined. The purpose of this article is to demonstrate the performance of MRSFF in oilseed rape planting area extraction. A practical method for defining the weighted values, the variance coefficient weight method, was proposed to set up criterion. Oilseed rape field canopy spectra from the whole growth stage were collected prior to investigating its phenological varieties; oilseed rape endmember spectra were extracted from the Hyperion image as identifying samples to be used in analyzing the oilseed rape field. Wavelength range divisions were determined by the difference between field-measured spectra and image spectra, and image spectral variance coefficient weights for each wavelength range were calculated corresponding to field-measured spectra from the closest date. By using MRSFF, wavelength ranges were classified to characterize the target's spectral features without compromising spectral profile's entirety. The analysis was substantially successful in extracting oilseed rape planting areas (RMSE ≤ 0.06), and the RMSE histogram indicated a superior result compared to a conventional SFF. Accuracy assessment was based on the mapping result compared with spectral angle mapping (SAM) and the normalized difference vegetation index (NDVI). The MRSFF yielded a robust, convincible result and, therefore, may further the use of hyperspectral imagery in precision agriculture. |
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| Keywords: | Hyperspectral imagery Multi range spectral feature fitting Oilseed rape Field-measured spectra Endmember |
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