Polarimetric SAR image terrain classification method based on self-supervised representation learning

Abstract

The invention provides a polarimetric SAR image terrain classification method based on self-supervised representation learning. The method mainly solves the problems that an existing polarimetric SARdeep convolutional network needs a large number of tags for classification and is poor in robustness. According to the scheme, the method comprises the following steps: performing polarization coherence matrix modal representation extraction and Pauli color modal representation extraction on polarimetric SAR original data; designing a self-supervised representation learning loss function and a network framework, and training the framework under the condition of not using label data; migrating the trained network framework weight value into a deep convolution integral type network model; carrying out fine tuning training on the deep convolutional integral network by using a small number of labeled samples to obtain a trained classifier; and inputting the test data into the trained classifier to obtain a final classification result. According to the method, the requirement for the label data volume is reduced, the classification precision and robustness of the polarimetric SAR deep convolutional network are improved, and the method can be used for guiding agricultural and marine monitoring.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a polarization SAR image ground object classification method, which can be used to guide agriculture and ocean monitoring. Background technique [0002] The task of classification of polarimetric SAR images is to divide each pixel of the acquired polarimetric SAR images into different terrain categories. It has been widely used and has broad prospects in the fields of urban planning, ocean monitoring, geological exploration, and crop growth assessment. [0003] At present, the polarimetric SAR object classification technology can be divided into three directions: the first is to classify the polarimetric SAR data based on the polarization scattering mechanism. Most of such classification methods are based on the polarization target decomposition theory of Pauli decomposition, Freeman decomposition and H / α decomposition to decompose the polarization target, s...

AI Technical Summary

Problems solved by technology

However, training classifiers based on deep convolutional networks requires a large amount of labeled data

When the label data is limited, the trained classifier has low classification accuracy and poor robustness

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