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：2004
卷号：XXXV Part B1
页码：216-221
出版社：Copernicus Publications
摘要：This work proposes a framework for increasing the degree of automation of low-resolution satellite images interpretation procedures. Basically, the method starts with an image segmentation which, according to a criterion of spectral response homogeneity, outlines the regions to be classified. The classification procedure is aided by expert knowledge. This procedure makes use of three types of knowledge: spectral, which relates the homogeneous classes of spectral response to the correspondent classes of interest; contextual, indicating the relevant contexts for the discrimination of classes with similar spectral responses; and multitemporal reasoning, considering both the former classification of the region and the plausible class transitions in that particular time interval. This strategy takes simultaneously into account spectral, contextual and multitemporal evaluations of the region which, are combined into a single membership value. Each membership value corresponds to a class of interest, and the highest indicates its classification. As a consequence, the proposed model requires as input: satellite images of the region of interest acquired in different dates; the accurate classification of the former image and the previous mentioned categories of expert knowledge. The prominent points of the proposed methodology are: its flexible structure, which allows for straightforward application of this model to low-resolution image interpretation cases; and the automatic learning/calibration of the spectral levels to the current time image. Experiments were performed in order to evaluate the potential of the proposed framework. The images employed in the experiments are situated in the Taquari Watershed, more exactly, in the County of Alcinópolis that belongs to the State of Mato Grosso do Sul, Brazil. The experimental results indicate that the use of knowledge can contribute to the increment of the degree of automation of the interpretation process
