Small-sample polarized SAR ground feature classification method based on deep convolutional twin network

Abstract

The invention discloses a small-sample polarized SAR ground feature classification method based on a deep convolutional twin network, and mainly solves a problem that a conventional method is low in classification precision because the number of polarized SAR data mark samples is smaller. The method of the invention comprises the steps: 1), inputting a to-be-classified polarized SAR image and a real ground object mark of the to-be-classified polarized SAR image, and carrying out the Lee filtering; 2), extracting an input feature vector from the filtered to-be-classified polarized SAR data, andcarrying out the dividing of a training sample set and a test sample set; 3), carrying out the combination of each two samples in the training sample set, and obtaining a sample pair training set; 4), building the deep convolutional twin network, and carrying out the training of the deep convolutional twin network through the training sample set and the sample pair training set; 5), carrying outthe classification of the samples in the test set through the trained deep convolutional twin network, and obtaining the classes of ground features. According to the invention, the method expands thetraining set under the twin configuration, achieves the extraction of the difference features, enables the classification precision of a model to be higher, and can be used for the target classification, detection and recognition of a polarized SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for classifying polarimetric SAR ground features, which can be used for ground feature classification or target recognition of polarimetric SAR remote sensing images. Background technique [0002] Polarization SAR is a synthetic aperture radar capable of full-polarization measurement of the target. It performs full-polarization measurement and imaging of the target by simultaneously transmitting and alternately receiving combined echoes of different polarization states. Polarimetric SAR data contains richer target scattering information, can express and describe the target more comprehensively, and improve the ability to identify ground objects. At the same time, it has the advantages of all-weather, all-time, high resolution, etc. It has outstanding advantages in aspects such as recognition, object classification, and parameter inversion, so it is w...

AI Technical Summary

Problems solved by technology

Although this method makes full use of the target decomposition and statistical distribution characteristics of polarimetric SAR data, it still has the disadvantages of complex feature design, strong expert knowledge and more labeled samples for model training.

Although this method makes full use of the supervision information of labeled samples of polarimetric SAR data and improves the classification accuracy, the method still has the disadvantage that the training process requires a large amount of labeled sample information, and the cost is high. In less cases, the classification accuracy of the network will be greatly reduced

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