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Classification of remote sensing imagery provides an inexpensive yet efficient approach to land cover mapping. In supervised image\udclassification, training samples are collected through certain sampling schemes, which are used to derive classification rules, aiming\udfor adequate accuracy for the applications at hand. However, in conventional classification methods, the potential of training\udsamples in terms of locational information is not tapped further, confounding the classification accuracy to the limited separability\udinherent to the given input feature vector. This paper explores two methods pertaining to geostatistics, i.e., simple kriging with local\udmean and cokriging, to predict class occurrences based on training samples' indicator transforms (location and classes) and\udspectrally derived class probabilities, thus calibrating the a posterior class probability vectors derived from initial spectral\udclassification. The results showed that classification accuracy is significantly increased by these two methods for utilizing spatial\udinformation contained in training samples and initial spectral classification, compared with those obtainable with spectral\udclassification. Moreover, the proposed methods constitute a valuable strategy for making fuller use of information residing in\udtraining data for improving spectrally derived classification, which is independent of the specific classifiers initially adopted for\udimage classification

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

POST-CLASSIFICATION APPROACH BASED ON GEOSTATISTICS TO REMOTE SENSING IMAGES : SPECTRAL AND SPATIAL INFORMATION FUSION