Semi-supervised classification and regional distance measurement based SAR (Synthetic Aperture Radar) image identification method

Abstract

The invention discloses a semi-supervised classification and regional distance measurement based SAR (Synthetic Aperture Radar) image identification method. The implementation steps of the semi-supervised classification and regional distance measurement based SAR image identification method comprise establishing an image library through original SAR image segmentation and selecting SAR image blocks which are single in target from the image library; extracting feature vectors of the image blocks in the image library; dividing the selected SAR images into a plurality of classes, utilizing the corresponding feature vectors to serve as a training sample, training a semi-supervised classifier and performing classification on the image library through the semi-supervised classifier; obtaining the classes of the query image blocks through the trained classifier, wherein the query image blocks are input by users; calculating a class set of the query image blocks according to a confusion matrix, calculating regional similarity distances between the query image blocks and image blocks and returning the user required number of image blocks according to the order that the distances are from small to large, wherein the image blocks belong to the class set in the image library. According to the semi-supervised classification and regional distance measurement based SAR image identification method, the classification error can be corrected, the information identification accuracy is high, and interpretation can be performed on a plurality of SAR images simultaneously.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for recognizing SAR images, which can be applied to simultaneous interpretation of multiple SAR images. Background technique [0002] Because of the all-day and all-weather detection capability of SAR images, especially the fact that relative optical images are completely independent of weather factors, its application fields are gradually expanding, including military, agriculture, navigation, geographic surveillance, etc. The segmentation, denoising, and change detection of SAR images are all research hotspots, and an important basis of these research fields is SAR image recognition. Some traditional recognition technologies are 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. However, these technologies are obviously not suitable for the current application envi...

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.on Pattern Analysis and Machine Intelligence, 2001, 23(9): 947-963, the SIMPLIcity retrieval system has been successfully applied to massive natural image retrieval problems, but due to technical limitations and the characteristics of SAR images, it is directly applied to SAR image recognition. 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.In Synthetic Aperture Radar,2009.APSAR2009.2nd Asian-Pacific Conference on(pp.796-799).IEEE, this method is oriented to SAR images, and texture features are effectively used in the retrieval process. However, 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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