Remote sensing image clustering method

Abstract

The invention discloses a remote sensing image clustering method and belongs to the technical field of image analysis. The remote sensing image clustering method comprises the following steps: A, determining the number of optimal clustering centers of an original image; B, acquiring the multi-scale expression of the original image through a Gaussian convolution function, and mapping the original image into a scale space thereof to produce a multilayer document set; C, establishing a dirichlet distribution model with invariant overlapping image semanteme according to the multilayer document set, and estimating the mixed proportion parameter of the theme of each document in the multilayer document set and the distribution parameters of the theme which produces visual words according to the probability; and D, obtaining the clustering category of each visual word according to the posteriori probability maximation method. The calculation complicity due to the generation of the document set in advance is avoided, the correlation of the documents can be kept and the detection efficiency of the geographical target of the remote sensing image is improved.

Description

technical field [0001] The invention relates to the technical field of image analysis, in particular to a clustering method for remote sensing images. Background technique [0002] Latent Dirichlet Allocation (LDA) is a probabilistic topic model proposed by Blei et al. in 2003 for text modeling. With the help of the expression of the probabilistic graphical model, it can model the conditional probability relationship between "word", "document" and "topic", and fully mine the probabilistic semantic information at the two levels of document and word. [0003] It is generally believed that the first probabilistic topic model proposed by Hoffmann in 1999 was based on the latent semantic analysis model (Latent Semantic Analysis, LSA), abandoning the original complex singular value decomposition analysis method, and constructing it from the perspective of a generative model The Probabilistic Latent Semantic Analysis (pLSA) model of , which has been successfully applied to text an...

AI Technical Summary

Problems solved by technology

Generally speaking, the acquisition of image objects often depends heavily on the segmentation algorithm to obtain the quality of the segmented plaques, and image segmentation is a difficult problem in the field of image processing at present, and there is no good general image segmentation algorithm.

Generally speaking, there are currently many clustering algorithms that can utilize spatial information to a certain extent, but for the consideration of semantic information between pixels, few such algorithms are currently applied to remote sensing image clustering analysis

[0009] To sum up, the existing clustering algorithms for remote sensing images need to generate a vast set of documents in advance, which has high computational complexity, high storage overhead, and poor correlation between documents. Low detection efficiency

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