Identifying saltcedar with hyperspectral data and support vectormachines |
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| Authors: | Reginald S Fletcher James H Everitt Chenghai Yang |
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| Institution: | 1. Department of Agriculture , USDA-ARS , 2413 E. Highway 83, Weslaco, Texas, 78596, USA reginald.fletcher@ars.usda.gov;3. Department of Agriculture , USDA-ARS , 2413 E. Highway 83, Weslaco, Texas, 78596, USA |
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| Abstract: | Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover types in west Texas. Spectral measurements were collected with a ground-based hyperspectral spectroradiometer (spectral range 350–2500 nm) in December 2008 and April 2009. Spectral data consisting of 1698 spectral bands (400–1349, 1441–1789, 1991–2359 nm) were subjected to a support vector machine classification to differentiate saltcedar from other vegetative and non-vegetative classes. For both dates, a linear kernel model with a C value (error penalty) of 100 was found optimum for separating saltcedar from the other classes. It identified saltcedar with accuracies ranging from 95% to 100%. Findings support further exploration of hyperspectral remote sensing technology and SVM classifiers for differentiating saltcedar from other cover types. |
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| Keywords: | support vector machine hyperspectral saltcedar Tamarix spp |
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