Classification Method of Polarization SAR Image Based on Spatial Information

Abstract

The invention discloses a polarization SAR image classification method based on spatial information, which mainly solves the problem that the classification accuracy of the existing unsupervised polarization SAR classification method is not high, and the number of classification target categories cannot be adaptively selected. The implementation steps are: 1. Filter the polarimetric SAR image; 2. Perform Pauli decomposition on the filtered polarimetric SAR image to obtain the pseudo-color image of the polarimetric SAR data; 3. Use the improved SLIC superpixel algorithm to filter the pseudo-color The color image is over-segmented to obtain K superpixel blocks; 4. Perform fast density peak clustering on K superpixel blocks to obtain the pre-classification result of the image; 5. Iterate the Wishart classifier for the entire image obtained by pre-classification classification to get the final classification result. Experiments show that the classification effect of the present invention is better and can be used for unsupervised classification of various polarization SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to a polarimetric SAR image classification method, and can be used for target recognition. Background technique [0002] Synthetic Aperture Radar (SAR) is an active microwave remote sensor that can provide all-weather and all-weather imaging characteristics. It can image areas covered by vegetation, deserts or shallow water, and can be used in military, agriculture, navigation, and geographical surveillance. and many other fields. Compared with SAR, polarimetric SAR performs full polarimetric measurement, which can obtain richer information about the target. In recent years, the classification using polarimetric SAR measurement data has been highly valued in the international remote sensing field, and has become the main research direction of image classification. Classical polarization SAR classification methods include: [0003] 1. Lee et al proposed the H / α-Wishart unsuperv...

AI Technical Summary

Problems solved by technology

However, there are two defects in this method: one is that the number of classification categories is fixed, the classification of complex areas is inflexible, and the classification accuracy is low; the other is that this method only considers the statistical information of pixels, and does not consider The spatial relationship among them, the regional consistency of the classification results is poor

This algorithm combines Freeman decomposition and the distribution characteristics of polarimetric SAR data, and effectively improves the classification effect of polarimetric SAR images. However, due to the division and merging of multiple categories in this method, its computational complexity is high, and This method still does not consider the spatial relationship between pixels

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