SAR image target detection method based on full convolutional neural network

A convolutional neural network and target detection technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problems of long detection time and low accuracy, and achieve the effect of improving accuracy and speed

Active Publication Date: 2017-10-03
XIDIAN UNIV
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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

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  • SAR image target detection method based on full convolutional neural network
  • SAR image target detection method based on full convolutional neural network
  • SAR image target detection method based on full convolutional neural network

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[0055] Below in conjunction with accompanying drawing, implementation steps and experimental effects of the present invention are described in further detail:

[0056] refer to figure 1 , the specific implementation steps of the SAR image target detection method based on the fully convolutional neural network of the present invention are as follows:

[0057] Step 1, obtaining the SAR image specifically includes the following steps:

[0058] (1a) Select a part of the target slice from the data in the MSTAR database as the target training set, and the other part as the target test set;

[0059] (1b) Randomly select 15 scene graphs from the data in the MSTAR database as the scene training set, and the remaining scene graphs as the scene test set;

[0060] Step 2, expanding the target training data set, specifically includes the following steps;

[0061] (2a) Randomly select 3000 groups of target slices from the target test set, with 15 slices in each group;

[0062] (2b) For...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/08G06V20/13G06F18/214
Inventor 焦李成屈嵘侯瑶淇马文萍杨淑媛侯彪刘芳尚荣华张向荣张丹唐旭马晶晶
Owner XIDIAN UNIV
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