SAR image recognition method based on semi-supervised classification and region distance measure

Abstract

The invention discloses a SAR image recognition method based on semi-supervised classification and region distance measurement. The implementation steps are: establish an image library by segmenting the original SAR image, select a single target SAR image block from the image library; extract the feature vector of the image block in the library; divide the selected SAR image blocks into several categories, and use the corresponding The feature vector is used as a training sample to train a semi-supervised classifier, and use this classifier to classify the image library; for the query image block input by the user, use the trained classifier to obtain its category; obtain the category set of the query image block according to the confusion matrix , calculate the area similarity distance between the query image block and the image blocks belonging to the set in the image database, and return the number of image blocks required by the user in the order of the distance from small to large. The invention has the advantages of correctable classification errors and high information recognition accuracy, and can be used to simultaneously interpret multiple SAR images.

Description

Technical field [0001] The invention belongs to the technical field of image processing and relates to a SAR image recognition method, which can be applied to simultaneously interpret multiple SAR images. Background technique [0002] Because SAR images have all-time and all-weather detection capabilities, especially the fact that optical images are completely independent of weather factors, their application areas are gradually expanding, including military, agriculture, navigation, and geographic surveillance. SAR image segmentation, denoising, change detection, etc. are all research hotspots, and an important foundation of these research fields is SAR image recognition. Some traditional recognition techniques mainly focus on the problem of recognition accuracy, and most of them are applied to the problem of small area recognition of a single SAR image. However, these technologies are obviously not in line with the current application environment where the number of SAR images...

AI Technical Summary

Problems solved by technology

So far, many mature and well-known retrieval systems have been proposed, such as SIMPLIcity retrieval system, see James Z.Wang, Jia Li, Gio Wiederhold.SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Llbraries.IEEE Trans.onPattern Analysis and Machine Intelligence, 2001, 23(9): 947-963, the SIMPLIcity retrieval system has been successfully applied to a large number of natural image retrieval problems, but due to technical limitations and the characteristics of SAR images, the effect of directly applying it to SAR image recognition is not satisfactory. not ideal

Another example is the SAR image retrieval system combined with Gaussian mixture model classification proposed in 2009, 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.InSynthetic 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, but Due to the use of supervised classification methods, its generalization ability in real 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 obtained cannot fully verify the effectiveness of the method.

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