文章基本信息

标题：Object-Based Classification Using High Resolution Satellite Data as a Tool for Managing Traditional Japanese Rural Landscapes
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作者：K. Takahashi ; N. Kamagata ; K. Hara 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B4
页码：1359-1364
出版社：Copernicus Publications
摘要：A system for object-based classification of landscape types incorporating topographic data was designed and tested in a Japanese countryside target area. IKONOS data (Japan Space Imaging - multi-spectral resolution 4 meter, panchromatic resolution 1 meter) acquired on 23 April 2001 was utilized, and the classification used Definiens Ver.5 software (Definiens). Initial segmentation was a muli-resolution, bottom-up system, and each segment identified was considered to be one object. Topographic data was derived from field surveys and topographic maps. Using a master landscape map for assessment of accuracy, two classifications, employing the same landscape types and ground truth, were implemented. One classification employed only the extant classification system based on spectral characteristics; while the other featured integration of the topographic data. The results showed that in terms of overall classification accuracy, the object-based using topographic data (56%) scored higher than the object-based alone (52%). In terms of overall Kappa index as well, object-based with topographic (0.51) outscored the object-based only (0.47). Several misclassifications seen in the extant system were eliminated by addition of the topographic data. These results indicate that object- based classification of very high resolution data combined with topographic data produces a system that promises to be an effective tool for landscape classification. The results also indicate that additional gains in accuracy may be achieved by developing a system for adjusting scale parameter according to the characteristics of individual landscape types
关键词：Ecology; Land Cover; Mapping; Classification; IKONOS; Object; Segmentation; Texture