SAR (Synthetic Aperture Radar) image analysis method based on self-adaptive fuzzy C mean-value clustering fuzzification

Abstract

The invention belongs to the field of the image data analysis technology and specifically discloses a SAR image analysis method based on self-adaptive fuzzy C mean-value clustering fuzzification. The method comprises the steps: firstly, processing and outputting the fuzzification result of a fuzzy decision table based on the present FCM (Fuzzy C Mean-value); secondly, gradually increasing the values of various category numbers Cm based on the value of the incompatible degree and outputting the corresponding fuzzification result of each condition attribute when the number Cm is increased by 1; when the incompatible degree is more than the preset threshold value, gradually adjusting the category number Cm of the corresponding condition attribute from the condition attribute with minimum importance degree; and finally, outputting the fuzzification pre-treatment result of the present SAR image based on the corresponding fuzzification result of the present category number Cm of each condition attribute. The SAR image analysis method is used for target identification of SAR images, the output fuzzification pre-treatment result is capable of remarkably improving the correct identification rate of the target.

Description

technical field [0001] The invention belongs to the technical field of image data analysis, in particular to a SAR image analysis method based on improved fuzzy C-means clustering fuzzification. Background technique [0002] Synthetic Aperture Radar (SAR) not only has the advantages of all-day and all-weather, but also has the characteristics of strong penetrating power and high resolution, so SAR images can describe target information in a more detailed and comprehensive manner. When performing target recognition on SAR images, feature extraction is performed first, and then classification training is performed based on the extracted feature information, and then target recognition on SAR images is completed. The current feature extraction of SAR images is mainly based on physical properties and mathematical transformations. The features based on physical properties can be subdivided into computer vision features and electromagnetic scattering features. Among them, comput...

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Problems solved by technology

[0006] For the fuzzy decision table after fuzzy preprocessing, if the number of target categories of each condition attribute is too large, although the compatibility of the original SAR image decision table can be maintained, the extracted decision rules are too complicated, thus Affect the work efficiency of the system; if it is too small, although the extracted decision rules can be made more concise, it will cause too much loss of original information and introduce too many contradictory objects. Based on this, it is necessary to modify the existing FCM processing method Make improvements so that it can adaptively adjust the number of target categories for each conditional attribute under the premise of considering the incompatibility of the fuzzy decision table, so as to improve the simplicity and accuracy of system processing

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