Polarized SAR image classification method based on dual-channel convolutional network

Abstract

The invention discloses a polarized SAR image classification method based on a dual-channel convolutional network. The method comprises the steps of filtering a to-be-classified polarized SAR image; extracting a multi-dimensional feature vector from a coherence matrix of each pixel point of the filtered polarimetric SAR image; performing spatial weighting on the polarized SAR image; randomly selecting a training sample and a test sample for each surface feature type of the polarized SAR data according to the real surface feature mark; constructing a multilayer convolutional network model; inputting the training sample into a multilayer convolutional network model to obtain a trained convolutional network model; inputting the test sample into the trained convolutional network model to obtain a classification result of each pixel in the test sample; comparing the classification result with a real surface feature mark, and calculating a correct rate; outputting the classification results.The method has higher classification accuracy on the ground objects, the homogeneous region is more complete, the region consistency and the classification performance are better, and the method is suitable for the ground object classification and target recognition on the polarized SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to classification of polarimetric SAR images, in particular to a method for classification of polarimetric SAR images based on a dual-channel convolution network, which can be used for object classification and target recognition of polarimetric SAR images. Background technique [0002] With the development of remote sensing technology in the fields of satellites, manned spaceflight, and lunar exploration projects, polarimetric SAR has become the development trend of SAR, and polarimetric SAR can obtain more abundant target information. Hydrology and oceanography have extensive research and application value, such as topographic mapping, resource exploration, disaster monitoring and astronomical research and many other fields. The purpose of polarimetric image classification is to determine the class to which each pixel belongs, using polarimetric data obtained from ai...

AI Technical Summary

Problems solved by technology

One defect of H / α classification is that the division of regions is too arbitrary. When the data is distributed on the border of the region, it may be wrongly divided. In addition, the same type of ground objects may be divided into different regions. At the same time, the same region There may also be different types of ground objects in the

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