Image Processing and Classification Procedures for Analysis of Sub-decimeter Imagery Acquired with an Unmanned Aircraft over Arid Rangelands

Abstract

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.