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In automatic/semiautomatic mapping of land use/cover using very high resolution remote-sensing imagery, the major challenge is that a single class of land use contains ground targets with varied spectral values, textures, geometries and spatial features. Here we present an object-oriented strategy for automatic/semiautomatic classifications of land use/cover using very high resolution remote-sensing data. The strategy consists of character detecting, object positioning and coarse classification, then refining the classification result step by step. The strategy combines the form classification of the objects located on the same level by using spectral values, textures and geometric features with function classification by using spatial logic relationships existing among the objects on the same level or between different levels. Furthermore, it overcomes the problem of transformation from form classification to function classification and unifies land use classification and land cover classification organically. Such an approach not only achieves high classification accuracy, but also avoids the salt-and-pepper effect found in conventional pixel-based procedures. The borderlines of the classification result are clear, the patches are pure, and the classification objects exactly match the ground targets distributed across the study site. A feasible technical strategy for the large-scale application is discussed in this article.
