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Image Processing and Classification Procedures for Analysis of Sub-decimeter Imagery Acquired with an Unmanned Aircraft over Arid Rangelands
Unmanned aerial systems (UAS) have great potential as a platform for acquiring very high resolution aerial imagery for vegetation mapping. However, image processing and classification techniques require adaptation to images obtained with low-cost digital cameras. We developed and evaluated an image processing workflow that included the integration of resolution-appropriate field sampling, feature selection, and object-based image analysis for the purpose of classifying rangeland vegetation from a five-centimeter-resolution UAS image mosaic. Classification tree analysis was used to determine the optimal spectral, spatial, and contextual features. Segmentation and classification rule sets were developed on a test plot and were applied to the remaining study area, resulting in an overall classification accuracy of 78% at the species level and 81% at the structure-group level. The image processing approach provides a roadmap for deriving quality vegetation classification products from UAS imagery with very high spatial, but low spectral resolution.
Laliberte, Andrea S.
GIScience & remote sensing 2011 Jan-Mar, v. 48, no. 1
Journal Articles, USDA Authors, Peer-Reviewed
Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.
Agricultural Research Service
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