SAR image target detection method based on full convolutional neural network

Abstract

The invention discloses an SAR image target detection method based on a full convolutional neural network. The method mainly solves the problem of low accuracy and slow detection speed in the prior art, and is characterized by obtaining an SAR image; expanding a training dataset; constructing a nine-layer full convolutional neural network; training the full convolutional neural network through the expanded training dataset; inputting a test image into a trained model for significance test to obtain an output significance feature graph; carrying out morphological processing on the significance feature graph; carrying out connected domain labeling on the processed feature graph; with the mass center of each connected domain being the center, extracting a detection slice corresponding to each target mass center; and labeling each detection slice in the input original SAR image and obtaining a target test result of the test data. The full convolutional neural network is applied to SAR image target detection, thereby improving SAR image target detection speed and accuracy; and the method can also be used for object identification.

Description

【Technical field】 [0001] The invention belongs to the technical field of image processing, and in particular relates to a SAR image target detection method based on a fully convolutional neural network. 【Background technique】 [0002] Synthetic Aperture Radar (SAR) is a high-resolution active microwave remote sensing imaging radar, which has the advantages of all-weather, all-time, and high resolution. It is not limited by time and weather, and can obtain richer information on targets. It has been widely used in fields such as earth observation and military reconnaissance. [0003] SAR image target detection is a key step in SAR-ATR (Automatic Target Recognition), and it is also a hot spot in the application of SAR image interpretation. In SAR images, the target volume is small and the background information is complex, which makes target detection difficult. [0004] As one of the most widely used deep neural networks, convolutional neural network has become a research ho...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problems of long detection time and low accuracy of the SAR image target detection method, and propose a SAR image target detection method based on a fully convolutional neural network, which can realize end-to-end detection and improve detection accuracy rate and detection speed

Images