Polarized SAR image classification method based on ACGAN

Abstract

The invention discloses a polarized SAR image classification method based on ACGAN, and the method comprises the steps: carrying out the Pauli decomposition of a polarized scattering matrix, and building a feature matrix based on pixel points; replacing each element in the feature matrix with an image block of a neighborhood of the element to obtain a feature matrix based on the image block; constructing a training data set by using the feature matrix based on the image blocks, and training the ACGAN network model by using the training data set to obtain a pixel-level classification result; and finally, converting the feature matrix into an RGB pseudo-color image, and dividing the image into K super-pixel areas by using an SLIC super-pixel algorithm. And a final classification result is optimized by combining the pixel-level classification result and the super-pixel block. According to the method, the polarization scattering information and the spatial neighborhood information of the polarization SAR data are fully utilized, and the generative adversarial network with the auxiliary classifier is used for competitive adversarial training, so that the classifier can extract classification features more effectively, and higher classification precision is obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, and specifically relates to an ACGAN (Auxiliary ClassifierGAN)-based polarimetric synthetic aperture radar (SAR) image classification method, and the invention can be applied to ground object classification and target recognition of polarimetric SAR images and other tasks. Background technique [0002] Polarization SAR is a multi-parameter, multi-channel remote sensing imaging system, which has the advantages of all-day, all-weather, high resolution, and large-area coverage. It can obtain more information about targets, and is widely used in remote sensing and map mapping. and other fields. Polarimetric SAR ground object classification is an important way to interpret polarimetric SAR data. The polarimetric measurement data obtained by airborne or spaceborne polarimetric SAR sensors is used to determine the type of ground objects to which each pixel belongs. In forestry , agriculture, ...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide an ACGAN-based polarimetric SAR image classification method for the deficiencies in the above-mentioned prior art. SAR data information enables the classifier to extract classification features more effectively to solve the technical problem of low image classification accuracy existing in the prior art

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