Polarized SAR Classification Method Based on Semi-supervised Deep Sparse Filtering Network

Abstract

The invention discloses a semi-supervised deep learning method based on semi-supervised sparse filtering. It solves the technical problem of complex parameter adjustment of traditional deep learning methods and low classification accuracy when there are low-label data. The steps include: inputting polarimetric SAR image data to be classified; extracting training samples and test samples; calculating the training sample Wishart nearest neighbor samples; initialize the parameters of the deep sparse filter network; pre-train the deep sparse filter network; fine-tune the deep sparse filter network; predict the category of the test sample; output the classification image and classification accuracy of the polarimetric SAR image to be classified. By constructing a novel deep sparse filter network model and adding a semi-supervised regular term in the pre-training process, the present invention reduces the complexity of deep learning network parameter adjustment and improves the classification accuracy of polarimetric SAR images. It can be used in technical fields such as environmental monitoring, earth resource surveying and military systems.

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, in particular to a polarization SAR classification method based on a semi-supervised deep sparse filter network. It can be used in environmental monitoring, earth resource surveying and military systems, etc. Background technique [0002] Machine learning (Machine Learning, ML) is a multi-field interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. Specializes in the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills, and reorganize existing knowledge structures to continuously improve their performance. In the field of polarimetric SAR image classification, there have been many breakthroughs in machine learning, such as Wishart maximum likel...

AI Technical Summary

Problems solved by technology

For example, learning rates, momentum, sparsity penalties, etc., and the final determination of these parameters needs to be obtained through cross-validation, which requires a lot of time and effort

With the continuous development of the field of remote sensing, applications such as environmental monitoring, earth resource surveying, and military systems have increased demand for polarimetric SAR image processing. It is desired to achieve ideal results for the classification of polarimetric SAR images, although deep learning is There are obvious advantages in machine learning methods, but the traditional deep learning network requires a large number of parameter adjustments, which will consume a lot of time. Improper parameter selection will directly affect the performance of the deep learning network and the final classification results, which restricts Application of deep learning methods in the field of remote sensing

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