Polarized SAR image random forest classification method integrating multiple features

Abstract

The invention discloses a polarized SAR image random forest classification method integrating multiple features. The method comprises the steps that an SLIC superpixel generation algorithm is utilizedto segment a to-be-classified polarized SAR image; feature information of the polarized SAR image is extracted, and a high-dimensional polarized feature map is constructed; a random forest model is trained based on the high-dimensional polarized feature map, and a polarized SAR image random forest model is constructed; the polarized SAR image random forest model is utilized to perform statisticalanalysis on the number of votes for each category of pixels, and a superpixel category probability graph based on a random forest is constructed with the superpixels being units; each superpixel category probability is iteratively corrected based on a PLR model, and a superpixel category probability graph after iterative correction is obtained; and each superpixel category is calculated with thesuperpixels being units, and a classification result is output. According to the method, the improved SLIC algorithm is utilized to generate accurate and fine superpixels as classification units, so that interference of speckle noise in the polarized SAR image is effectively lowered; and by use of neighborhood features of the superpixels, the interference of the speckle noise is further reduced, and the precision of the classification result is improved.

Description

technical field [0001] The present invention relates to the technical field of polarization synthetic aperture radar image classification methods, in particular to a comprehensive multi-feature polarization SAR image random forest classification method. Background technique [0002] Synthetic Aperture Radar (SAR) is widely used in target detection and surveying and mapping due to its all-day and all-weather imaging capabilities. By transmitting and receiving electromagnetic waves in different polarization states, polarimetric SAR images can obtain rich ground feature information, so it has obvious advantages in ground feature classification. However, the specific coherent speckle noise in polarimetric SAR images will interfere with the classification accuracy of ground objects. Comprehensive utilization of polarization features and spatial neighborhood feature classification is an effective method to suppress coherent speckle noise and improve classification accuracy. [00...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a comprehensive multi-feature polarization SAR image random forest classification method to solve the problem that the polarization SAR image classification accuracy is greatly disturbed by coherent speckle noise, and to obtain accurate and continuous polarization SAR image classification results

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