Polarized SAR land feature classification method based on full convolution neural network

Abstract

The invention discloses a polarized SAR land feature classification method based on a full convolution neural network, comprising: performing Pauli decomposition on a to-be-classified polarized scattering matrix S to obtain the odd scattering coefficient, the even scattering coefficient and the volume scattering coefficient; using the odd scattering coefficient, the even scatter coefficient and the volume scattering coefficient as the three-dimensional image characteristics F of the polarized SAR image; then converting the obtained three-dimensional image characteristics matrix F into an RGB image F1; randomly selecting m x n pixel blocks in the RGB image F1 as training samples; using the whole RGB image F1 as a testing sample; re-constructing a full convolution neural network model; training the training samples through the full convolution neural network to obtain a trained model; and then, through the trained model, classifying the test set and obtaining the classification result. The method of the invention can solve the problem of low time efficiency in the prior art and shorten the running time under the condition of high classification accuracy.

Description

【Technical field】 [0001] The invention belongs to the technical field of image processing, and in particular relates to a polarimetric SAR image classification method, which can be used for polarimetric SAR image object classification and target recognition, in particular to a polarimetric SAR object classification method based on a fully convolutional neural network. 【Background technique】 [0002] As a hot research field of contemporary remote sensing technology, polarimetric SAR has many outstanding advantages, such as not being affected by time, and capable of 24-hour imaging. Polarization SAR images have unique advantages and broad application prospects, and have been successfully used in land use classification, change detection, surface parameter inversion, soil moisture and soil moisture inversion, artificial target classification, building extraction, etc. [0003] Chen Jun et al. comprehensively compared the polarization features obtained by polarization target dec...

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Problems solved by technology

[0005] In order to solve the problems existing in the prior art, the object of the present invention is to propose a polarization SAR object classification method based on a fully convolutional neural network, by which the classification time length of the traditional method and the traditional CNN network parameters can be solved. The problem of classification time can reduce the processing time of polarimetric SAR image classification under the premise of ensuring high accuracy

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