Shoreline information is fundamental for understanding coastal dynamics and for implementing environmental policy. The analysis of shoreline variability usually uses a group of shoreline indicators visibly discernible in coastal imagery, such as the seaward vegetation line, wet beach/dry beach line, and instantaneous water line. These indicators partition a beach into four zones: vegetated land, dry sand or debris, wet sand, and water. Unmanned aircraft system (UAS) remote sensing that can acquire imagery with sub-decimeter pixel size provides opportunities to map these four beach zones. This paper attempts to delineate four beach zones based on UAS hyperspatial RGB (Red, Green, and Blue) imagery, namely imagery of sub-decimeter pixel size, and feature textures. Besides the RGB images, this paper also uses USGS (the United States Geological Survey) Munsell HSV (Hue, Saturation, and Value) and CIELUV (the CIE 1976 (L*, u*, v*) color space) images transformed from an RGB image. The four beach zones are identified based on the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) textures. Experiments were conducted with South Padre Island photos acquired by a Nikon D80 camera mounted on the US-16 UAS during March 2014. The results show that USGS Munsell hue can separate land and water reliably. GLCM and LBP textures can slightly improve classification accuracies by both unsupervised and supervised classification techniques. The experiments also indicate that we could reach acceptable results on different photos while using training data from another photo for site-specific UAS remote sensing. The findings imply that parallel processing of classification is feasible.
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