Polarized SAR classification method based on deep learning of shallow-layer characteristics and T-matrix

Abstract

The invention discloses a polarized SAR classification method based on deep learning of shallow-layer characteristics and T-matrix. The polarized SAR classification method based on deep learning of shallow-layer characteristics and T-matrix mainly solves the problem that the prior art is low in the classification correct rate for the same natural object having obvious difference on the scattering information and different natural objects having similar scattering information. The polarized SAR classification method based on deep learning of shallow-layer characteristics and T-matrix includes the following implementation steps: 1, filtering an original polarized SAR image; 2, extracting the polarized shallow-layer characteristics of the polarized SAR image after filtering; 3, fusing the shallow-layer characteristics with the polarized SAR data after filtering to construct training simples and a test samples; 4, learning the training samples through a convolution neural network; and 5, classifying the test samples by means of the convolution neural network which is obtained through learning, and obtaining the final polarized SAR natural object classification result. The polarized SAR classification method based on deep learning of shallow-layer characteristics and T-matrix is high in the classification correct rate of the polarized SAR target natural objects, has good experimental effect on classification of the natural object targets of a large area, and can be applied to target identification and natural object classification of a large scene.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarimetric SAR ground object classification method, which is applicable to target recognition and large-scale ground object classification. Background technique [0002] With the rapid development of microwave remote sensing technology, high-resolution polarization synthetic aperture radar, as one of the typical representatives, will inevitably become a popular trend in the field of SAR. Although the high-resolution polarization SAR contains rich backscatter information, it has been found in practice that the complex scene information contained in real images cannot be fully expressed by only using shallow polarization features. The classification of polarimetric SAR images involves many disciplines such as physics, probability theory, pattern recognition, data mining, signal processing, etc. It is one of the important branches in the field of image processing....

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that different feature selection is required to obtain the same good classification accuracy for different data, which obviously greatly increases the workload of researchers, and the classic SAR image features are not very important for image expression. Sufficient, this will also have a certain impact on the classification results of the method

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