Polarimetric SAR image classification method based on feature attention and feature improvement network

Abstract

The invention provides a polarimetric SAR image classification method based on feature attention and a feature improvement network. The polarimetric SAR image classification method mainly solves the problems that an existing polarimetric SAR image classification method based on deep learning is poor in intra-area consistency and inconvenient for end-to-end classification. The implementation schemecomprises the following steps of: 1) inputting a to-be-classified polarized SAR image and filtering the to-be-classified polarized SAR image; 2) synthesizing a pseudo color image and a classificationlabel of the polarized SAR image; 3) extracting initial features of the polarimetric SAR image and preprocessing the features; 4) respectively constructing an input representation layer, a feature attention sub-network, an encoder and a decoder, and sequentially connecting the input representation layer, the feature attention sub-network, the encoder and the decoder to form a feature attention and feature improvement network; 5) training fa eature attention and feature improvement network; 6) Inputting the polarization SAR image into the trained network to obtain the classification result. The polarimetric SAR image classification method is high in intra-area consistency, low in noise and high in classification precision, end-to-end learning and classification are realized, and the polarimetric SAR image classification method can be used for polarimetric SAR image classification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarization SAR image classification method based on feature attention and feature improvement network, which can be used for polarization SAR image classification. Background technique [0002] Synthetic Aperture Radar (SAR) is an important technology for obtaining ground object information in recent years. Its main advantage is its ability to provide high-resolution image data in all weather conditions, regardless of day or night. In addition to the above characteristics, polarimetric synthetic aperture radar (SAR) can also use the backscattering of object polarized waves to form images. The classification problem of TSAR has gradually become a research that is both challenging and of great practical application value in the field of remote sensing. Polarization SAR classification is a pixel-level classification. It is necessary to accurately assign a catego...

AI Technical Summary

Problems solved by technology

Due to the strong feature expression ability of the deep learning model, the classification accuracy of polarimetric SAR is greatly improved, but the consistency and accuracy of the classification results are still insufficient, and it is difficult to avoid the noise visible to the human eye.

In order to further enhance the intra-regional consistency and improve the classification accuracy, many models have improved the existing models by adding steps such as pre-processing and post-processing. However, the process of such models is complicated and cannot be used for end-to-end learning and classification.

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