SPOT5 imagery is widely used in urban planning, investigation of land utilization, environmental management etc. for
its relatively high-resolution. Object-oriented classification techniques based on image segmentation are being actively
studied in the high-resolution image process and interpretation to extract a variety of thematic information. Different
from the pixel-based image analysis, the processing of the object-oriented method is based on image segment, not single
pixel. The object-oriented classification includes two consecutive processes. An image is subdivided into separated
regions according to the spectral and spatial heterogeneity in the image segmentation process. Then the objects are
assigned to a specific class according to the class's detailed description in the image classification process. As a case
study, the study area is a part of the Taiwan, whose mudstone bare-land is a significant problem due to the poor condition
of the soil-physical and microclimate. The SPOT-5 image in March of 2006 is segmented and then these segments are
classified to hierarchically linked objects by the eCognition software. By using manual interpreted aerial photo of the
same area and traditional classification results as reference for accuracy assessment, this study has higher accuracy
compared with the traditional classification.
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