Algorithms for clustering data
Algorithms for clustering data
Proceedings of the 2004 ACM symposium on Applied computing
FAÇADE: a fast and effective approach to the discovery of dense clusters in noisy spatial data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Discovering spatial patterns accurately with effective noise removal
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Spatial contextual classification and prediction models for mining geospatial data
IEEE Transactions on Multimedia
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Extracting accurate land use and land cover information from remote sensing data is a challenging problem due to the gap between theoretically available information in remote sensing imagery and the limited classification ability based on spectral analysis. Traditional classification techniques based on spectral analysis of single pixel usually produce "noisy" results that contain many wrongly classified pixels. This paper presents a novel post classification method to detect the pixels that are wrongly classified and reassign them to correct fields in spatial context. The strategy is demonstrated through the classification of a benchmark digital aerial photograph. The experimental results show that the proposed approach can produce a more accurate classification than previous approaches.