Semi-supervised classification of polarimetric SAR images based on DSFNN and non-local decision

Abstract

A semi-supervised classification method of polarimetric SAR images based on DSFNN and non-local decision is proposed. The method comprises steps: the data of polarimetric SAR images being input; superpixel segmentation of polarimetric SAR image; extracting original features and super-pixel features of each pixel in polarimetric SAR image; training sample set and test sample set being selected; using the training sample set to train the depth super-pixel filter network; the depth superpixel filter network being used to predict the test samples; based on non-local decision, the training set being expanded by selecting samples from the test set; updating the depth superpixel filter network; the trained network being used to classify the test samples, so that a classification result diagram isobtained. The depth super pixel filter network of the invention extracts super pixel features to overcome coherent speckle noise, and utilizes semi-supervised classification algorithm of non-local decision to reduce the number of training samples and effectively improve classification accuracy, and can be used in the technical fields of polarimetric SAR image ground object classification and target recognition and the like.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, in particular to a semi-supervised classification method for polarimetric SAR images, specifically a semi-supervised classification method based on DSFNN (Deep Superpixel Filtering Neural Network, deep superpixel filtering network) and non-local decision-making, It can be used in technical fields such as polarization SAR image object classification and target recognition. Background technique [0002] Polarization SAR is an active microwave imaging system, which has the advantages of all-weather, all-time, and variable side viewing angles. Full-polarization SAR data can obtain rich scattering information of ground objects, which plays an important role in improving the classification and recognition accuracy of ground objects. Polarimetric SAR image classification mainly extracts the category information of ground objects by analyzing the backscattering signals of ground ...

AI Technical Summary

Problems solved by technology

Because it is difficult to obtain the true category of ground objects in polarimetric SAR images artificially, the training samples are often limited, and polarimetric SAR images are easily affected by coherent speckle noise, which lead to poor classification results.

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