Remote sensing image segmentation method based on region clustering

Abstract

The invention discloses a remote sensing image segmentation method based on region clustering, belonging to the field of remote sensing image comprehensive utilization. The method comprises the following steps: carrying out region pre-segmentation by a MeanShift algorithm to remove noise and perform initial cluster on image elements; carrying out fuzzy clustering on images which are subject to the pre-segmentation by a fuzzy C-means algorithm (FCM), and initially inducing and identifying characteristics of each image object to obtain the probability that each object affiliates to some a category so as to constitute a land category probability space of the remote sensing images, thereby providing a basis of object combination for further region segmentation; and performing region segmentation in the probability space of clustering images, classifying image elements which are close in the probability space and similar in the category as the same objects by region labels. In the method of the invention, two defects in the existing segmentation method are overcome, the remote sensing images can be effectively and accurately segmented, segmentation tasks of the remote sensing images can be finished by batch by integration, and data support can be preferably provided for extraction of land information from the remote sensing images.

Description

technical field [0001] The invention relates to a remote sensing image segmentation method, in particular to a remote sensing image segmentation method that uses a fuzzy C-means (Fuzzy CMeans, FCM) algorithm for fuzzy clustering and a mean shift (Mean Shift) algorithm for region segmentation. Background technique [0002] Remote sensing image segmentation refers to the process of dividing the pixels in the remote sensing image into several specific regions that do not intersect each other. The use of remote sensing image segmentation technology in the automatic extraction of typical coastal zone remote sensing information has three meanings: first, the processed object has transitioned from a pixel to an object corresponding to the real world map spot, which is closer to the human observation data. Thinking logic; secondly, make different pixels in the same object have the same spectral characteristics, to a certain extent overcome the problem of different spectra of the sam...

AI Technical Summary

Problems solved by technology

This method has developed rapidly in recent years. Many new methods are more effective in segmenting specific images. Since there is no general theory, these methods have their own advantages and disadvantages. They are generally immature, and the accuracy of image segmentation still needs to be improved.

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