TAILIEUCHUNG - Báo cáo hóa học: " Research Article Multisource Images Analysis Using Collaborative Clustering"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Multisource Images Analysis Using Collaborative Clustering | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 374095 11 pages doi 2008 374095 Research Article Multisource Images Analysis Using Collaborative Clustering Germain Forestier Cedric Wemmert and Pierre Gancarski LSIIT UMR 7005 CNRS ULP University Louis Pasteur 67070 Strasbourg Cedex France Correspondence should be addressed to Germain Forestier forestier@ Received 1 October 2007 Revised 20 February 2008 Accepted 26 February 2008 Recommended by C. Charrier The development of very high-resolution VHR satellite imagery has produced a huge amount of data. The multiplication of satellites which embed different types of sensors provides a lot of heterogeneous images. Consequently the image analyst has often many different images available representing the same area of the Earth surface. These images can be from different dates produced by different sensors or even at different resolutions. The lack of machine learning tools using all these representations in an overall process constraints to a sequential analysis of these various images. In order to use all the information available simultaneously we propose a framework where different algorithms can use different views of the scene. Each one works on a different remotely sensed image and thus produces different and useful information. These algorithms work together in a collaborative way through an automatic and mutual refinement of their results so that all the results have almost the same number of clusters which are statistically similar. Finally a unique result is produced representing a consensus among the information obtained by each clustering method on its own image. The unified result and the complementarity of the single results . the agreement between the clustering methods as well as the disagreement lead to a better understanding of the scene. The experiments carried out on multispectral remote sensing images have shown that .

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