Domain adaptive Faster R-CNN (Recurrent Convolutional Neural Network) semi-supervised SAR (Synthetic Aperture Radar) detection method

Abstract

The invention discloses a semi-supervised SAR (Synthetic Aperture Radar) detection method based on domain-adaptive Faster R-CNN (Recurrent Convolutional Neural Network), which solves the problem thatthe SAR target detection performance is reduced under a small number of marked images. The method comprises the following steps: obtaining a source domain containing a label and target domain data ofa small number of labels; training an original Faster R-CNN by using the source domain data; constructing a domain adaptive Faster R-CNN, initializing the domain adaptive Faster R-CNN, and performingtraining by utilizing source domain and target domain data to obtain a trained model; and inputting the target domain test data into the trained model to obtain a detection result of the test data. According to the method, the domain adaptation Faster R-CNN is constructed, the domain adaptation and decoder module is additionally arranged, SAR target detection is assisted by the optical remote sensing image, dependence on the SAR image with the label is reduced, global information of target domain data is learned through the decoder module, and the detection performance is further improved. Themethod is applied to SAR image target detection.

Description

technical field [0001] The invention belongs to the technical field of radar image processing, and further relates to synthetic aperture radar (SAR, Synthetic Aperture Radar) image automatic target recognition, specifically a semi-supervised SAR target detection method based on domain adaptation Faster R-CNN, which can be used for SAR images Object detection, including vehicle detection for parking lots. Background technique [0002] Synthetic Aperture Radar (SAR) is an active microwave imaging radar, which can realize all-weather and all-weather real-time long-distance monitoring of stationary targets (such as ships, vehicles, etc.). With the rapid development of radar imaging technology, the field of SAR Automatic Target Recognition (ATR) is developing rapidly. Generally, SAR ATR includes the following three steps: target detection, target discrimination and target recognition. As the first step in the SAR ATR steps, target detection is mainly used to determine the posit...

AI Technical Summary

Problems solved by technology

However, Faster R-CNN requires a large amount of labeled training data for network training. When the labeled training data is less, the detection performance of Faster R-CNN drops significantly.

[0005] The existing target detection methods, whether it is traditional CNN or improved Faster R-CNN, are all fully supervised target detection methods. The problem is that a large number of labeled training samples are required for network training, and the labeled training samples In rare cases, these fully supervised target detection methods are prone to overfitting during training, resulting in a significant drop in SAR target detection performance on test data

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