Polarized SAR image classification method based on ACGAN

A classification method and image technology, applied in the field of image processing, can solve the problem of low image classification accuracy, and achieve the effects of improving representation ability, high classification accuracy, and strengthening local spatial correlation.

Inactive Publication Date: 2019-05-21
XIDIAN UNIV
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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 ar

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  • Polarized SAR image classification method based on ACGAN
  • Polarized SAR image classification method based on ACGAN
  • Polarized SAR image classification method based on ACGAN

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Embodiment Construction

[0049]The present invention provides a polarimetric SAR image classification method based on ACGAN, which performs Pauli decomposition on the polarimetric scattering matrix S to construct a feature matrix F based on pixels; then each element in the feature matrix F is used by its neighborhood Replace the image block to obtain the feature matrix F1 based on the image block; then use the feature matrix F1 based on the image block to construct a training data set T, use the training data set T to train the ACGAN network model, and input the data set F1 to the trained model , to obtain the pixel-level classification results; then convert the feature matrix F into an RGB pseudo-color image, and use the SLIC superpixel algorithm to divide the image into K superpixel regions; combine the pixel-level classification results and superpixel blocks to optimize the final classification results. This method uses generative adversarial networks with auxiliary classifiers to compete with each ...

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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, ...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
Inventor 侯彪焦李成聂惠敏马晶晶马文萍白静
Owner XIDIAN UNIV
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