Polarized SAR image convolutional neural network classification method based on spatial feature atlas

Abstract

The invention relates to a polarized SAR image convolutional neural network classification method based on a spatial feature atlas. The method comprises: polarized SAR image data are inputted and preprocessing is carried out to obtain a to-be-classified polarized SAR image; the type of the to-be-classified polarized SAR image is determined; preliminary polarized SAR image feature extraction is carried out on the to-be-classified polarized SAR image to obtain a preliminary image feature combination unit; a feature atlas of a training sample is obtained in the polarized SAR image based on a pixel point and a neighbourhood so as to obtain a training sample space; a convolutional neural network model is erected, a network parameter is adjusted, and the convolutional neural network model is trained by using the feature atlas of the training sample; a combined feature of the to-be-classified polarized SAR image is processed based on a pixel pint and a neighbourhood to obtain a preliminary feature atlas, thereby forming a testing sample space; and then feature extraction is carried out on the testing sample by using the convolutional neural network model and classification is also carried out. Therefore, the classification precision of the polarized SAR image is improved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and relates to a polarization SAR image classification method, in particular to a polarization SAR image convolutional neural network classification method based on a spatial feature map. Background technique [0002] As an advanced microwave remote sensing method, polarimetric SAR has the advantages of all-weather, all-time, high resolution, and large-area coverage. The full polarization method can obtain the scattering characteristics of ground objects, which improves the detection and recognition capabilities of SAR images for ground objects; More comprehensive and rich ground object information has broad application prospects in the field of remote sensing. With the improvement of the data acquisition ability and image resolution of the polarimetric SAR system, the target types in the image are more refined and diverse. The point targets in the low-resolution SAR imag...

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Problems solved by technology

[0005] In order to make up for the shortcomings of insufficient feature extraction and shallow classification in existing polarimetric SAR remote sensing image classification methods, and solve the problem of low classification accuracy of polarimetric SAR images, the present invention provides a polarimetric SAR image based on spatial feature maps Convolutional Neural Network Classification Methods

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