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 B3
页码：820-825
出版社：Copernicus Publications
摘要：Recent developments in sensor technology make possible Earth observational remote sensing systems with high spectral resolution and data dimensionality. As a result, the flow of data from satellite-borne sensors to earth-stations is likely to increase to an enormous rate. This paper investigates a new on-board unsupervised feature extraction method that reduces the complexity and costs associated with the analysis of multispectral images and the data transmission, storage, archival and distribution as well. Typically in remote sensing a scene is represented by the pixel-oriented features. It is possible to reduce data redundancy by an unsupervised object-feature extraction process, where the object-features, rather than the pixel-features, are used for multispectral scene representation. The proposed algorithm partitions the observation space into exhaustive set of disjoint objects. Then, pixels belonging to each object are characterized by object features. Illustrative examples are presented, and the performance of features is investigated. Results show an average compression more than 25, the classification performance is improved for all classes, and the CPU time required for classification is reduced by a factor of more than 25, and some new features of the scene have been extracted
