文章基本信息

标题：INTERPRETING IMAGES WITH GEODMA
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作者：Thales Sehn Korting ; Leila Maria Garcia Fonseca ; Gilberto Camara 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：2010
卷号：XXXVIII - 4/C7
出版社：Copernicus Publications
摘要：Object oriented analysis offers effective tools to represent the knowledge in a scene. Knowledge-based interpretation arises as an ef- fective way to interpret remote sensing imagery. In this approach, human's expertise is organized in a knowledge base to be used as input of automated interpretation processes, thus enhancing performance and accuracy, and reducing at the same time the subjectivity in the interpretation process. Some systems such as Definiens, ENVI-FX, and more recently, InterIMAGE, have incorporated useful tools to aid the object-oriented classification. In this context, this work presents a system for object image analysis, called Geograph- ical Data Mining Analyst (GeoDMA), which implements several mechanisms for automatic image interpretation. It performs image segmentation, objects feature extraction, supervised and unsupervised classification with raster, shape and cellular data sets. GeoDMA has been used for intra-urban land cover classification of high spatial resolution images and also to detect deforestation patterns in the Brazilian Amazon. Such different applications warrant the generalist behavior of the system. In this article, we discuss each module implemented in the system, the integration of objects obtained by segmentation to extract texture features and landscape metrics. We also show the main tools for data preprocessing, sample selection and feature visualization. Finally, one application of automatic urban interpretation is shown
关键词：Image Classification; Data Mining; Self-Organizing Maps; Decision Trees; Neural Networks