Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale,texture and image objects |
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| Authors: | Minho Kim Timothy A Warner Marguerite Madden Douglas S Atkinson |
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| Affiliation: | 1. Center for Remote Sensing and Mapping Science (CRMS), Department of Geography , The University of Georgia , Athens, GA, 30602, USA mhkim73@uga.edu mhkim73@gmail.com;3. Department of Geology and Geography , West Virginia University , Morgantown, WV, 26506-6300, USA;4. Center for Remote Sensing and Mapping Science (CRMS), Department of Geography , The University of Georgia , Athens, GA, 30602, USA;5. Marine Extension Service, The University of Georgia , Athens, GA, 30602, USA |
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| Abstract: | This study used geographic object-based image analysis (GEOBIA) with very high spatial resolution (VHR) aerial imagery (0.3 m spatial resolution) to classify vegetation, channel and bare mud classes in a salt marsh. Three classification issues were investigated in the context of segmentation scale: (1) a comparison of single- and multi-scale GEOBIA using spectral bands, (2) the relative benefit of incorporating texture derived from the grey-level co-occurrence matrix (GLCM) in classifying the salt marsh features in single- and multi-scale GEOBIA and (3) the effect of quantization level of GLCM texture in the context of multi-scale GEOBIA. The single-scale GEOBIA experiments indicated that the optimal segmentation was both class and scale dependent. Therefore, the single-scale approach produced an only moderately accurate classification for all marsh classes. A multi-scale approach, however, facilitated the use of multiple scales that allowed the delineation of individual classes with increased between-class and reduced within-class spectral variation. With only spectral bands used, the multi-scale approach outperformed the single-scale GEOBIA with an overall accuracy of 82% vs. 76% (Kappa of 0.71 vs. 0.62). The study demonstrates the potential importance of ancillary data, GLCM texture, to compensate for limited between-class spectral discrimination. For example, gains in classification accuracies ranged from 3% to 12% when the GLCM mean texture was included in the multi-scale GEOBIA. The multi-scale classification overall accuracy varied with quantization level of the GLCM texture matrix. A quantization level of 2 reduced misclassifications of channel and bare mud and generated a statistically higher classification than higher quantization levels. Overall, the multi-scale GEOBIA produced the highest classification accuracy. The multi-scale GEOBIA is expected to be a useful methodology for creating a seamless spatial database of marsh landscape features to be used for further geographic information system (GIS) analyses. |
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