Multi-scale fuzzy measure and semi-supervised learning based SAR (Synthetic Aperture Radar) image identification method

Abstract

The invention discloses a multi-scale fuzzy measure and semi-supervised learning based SAR (Synthetic Aperture Radar) image identification method and solves the problem that the SAR image identification accuracy in the prior art is low. The multi-scale fuzzy measure and semi-supervised learning based SAR image identification method comprises the following steps of establishing an image library by segmenting an original SAR image and selecting image blocks with single targets; extracting characteristic vectors of the image blocks in the image library; classifying the selected image blocks into a plurality of categories, enabling corresponding characteristic vectors to be served as training samples, training a semi-supervised classifier and classifying the image library through the classifier; obtaining categories of inquire image blocks input by a user through a trained classifier; obtaining a category set of the image blocks through a confusion matrix; calculating a multi-scale area fuzzy similarity between the inquire image blocks and the image blocks belong to the set and returning the number of user required image blocks according to a sequence from large to small. The multi-scale fuzzy measure and semi-supervised learning based SAR image identification method can correct the classification error, is high in information identification accuracy and can be applied to simultaneous explain of a plurality of SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for identifying SAR image information, which can be applied to simultaneous interpretation of multiple SAR images. Background technique [0002] Because SAR images have all-day and all-weather detection capabilities, especially the characteristics that optical images are completely independent of weather factors, the application fields of SAR images are gradually expanding, including agriculture, geographic surveillance, navigation, military, etc. SAR image fusion, segmentation, denoising, and change detection are all research hotspots, and SAR image recognition is an important basis for these research fields. The traditional recognition technology is mainly aimed at the problem of recognition accuracy, and most of them are applied to the small-scale area recognition problem of a single SAR image, such as the SAR segmentation method of spectral clustering integrat...

AI Technical Summary

Problems solved by technology

The above two retrieval methods have been successfully applied to massive natural image retrieval problems, but due to technical limitations and the characteristics of SAR images, the effect of directly applying them to SAR image recognition is not ideal

In 2009, a SAR image retrieval system combined with Gaussian mixture model classification was proposed, that is, the GMM retrieval system, see Hou, B., Tang, X., Jiao, L., & Wang, S. (2009, October). SAR image retrieval based on Gaussian Mixture Model classification.In Synthetic Aperture Radar,2009.APSAR 2009.2nd Asian-Pacific Conference on(pp.796-799).IEEE, this method is oriented to SAR images, effectively using texture features in the retrieval process, However, due to the use of supervised classification methods, its generalization ability in practical problems is low, and because the similarity matching technology of this method does not consider the characteristics of SAR images, the retrieval effect is not ideal.

Although the excellent experimental results are given in this article, these results rely on overlapping cutting original SAR images to build a gallery. The image blocks obtained by this strategy have a high degree of clustering characteristics, that is, the distance between samples in the same class is small, The distance between samples of different classes is very large. Such data distribution is often very different from the data distribution in practical applications. The experimental results cannot fully verify the effectiveness of the method.

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