Semi-supervised multi-spectral remote sensing image segmentation method based on spectral clustering

Abstract

The invention discloses a semi-supervised multi-spectral remote sensing image segmentation method based on spectral clustering; the segmentation process includes that: (1) the characteristics inputted to the multi-spectral sensing image are extracted; (2) N points without labels and M points with labels are randomly and evenly sampled from a multi-spectral sensing image with S pixel points to form a set n which is the summation of N and M, wherein M points with labels are used for creating pairing limit information Must-link and Cannot-link sets; (3) the sampled point set is analyzed through semi-supervised spectral clustering to obtain the class labels of the n (n=N+M) points; (4) the sampled n (n=N+M) points are used as the training sample to classify the rest (S-N-M) points through nearest-neighbor rule, each pixel point is assigned with a class label according to the class of the pixel point and is used as the segmentation result of the inputted image. Compared with prior art, the invention has good image segmentation effect, strong operability, improves the classification accuracy, avoids searching the optimum parameters through repeated test, has small limit on image size and is better applicable to the segmentation of multi-class multi-spectral sensing images.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to multispectral remote sensing image segmentation, and can be used for preprocessing the multispectral remote sensing image. Background technique [0002] Multi-spectral remote sensing image analysis technology comprehensively utilizes multi-band satellites. Compared with traditional remote sensing image analysis technology, it has unique advantages in the detection of ground targets, especially stationary targets, and has better interpretation and identification of various natural phenomena and processes. Good application prospects. Segmentation of remote sensing images is one of the key technologies in the analysis and application of remote sensing images. Fast and high-precision multispectral remote sensing image segmentation methods are the prerequisites for various practical applications. [0003] At present, there are many segmentation methods for multispectral remote sen...

AI Technical Summary

Problems solved by technology

However, when the semi-supervised spectral clustering algorithm is applied to the segmentation of multispectral remote sensing images, it will face the problem of large amount of data. Williams et al. Spectral clustering of the approximation method, which uses a small number of representative points to approximate the eigenvector of the entire matrix. Although the calculation speed is fast, the classification error is large, and the image size is limited, and the effect is not good for multi-classification problems.

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