Deep learning-based SAR remote sensing image water surface target detection method

A technology of water surface target and detection method, which is applied in the field of SAR image water surface target detection based on deep learning, can solve the problems of slow detection speed and low accuracy rate of SAR image target, and achieve the effect of fast detection speed and high detection accuracy rate

Inactive Publication Date: 2018-05-18
NANJING UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to propose a method for detecting water surface targets in SAR remote sensing images based on deep learning, which overcomes the problems of slow detection speed and low accuracy of SAR image target detection in the prior art, and realizes accurate end-to-end detection of water surface targets in SAR images. detection

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  • Deep learning-based SAR remote sensing image water surface target detection method
  • Deep learning-based SAR remote sensing image water surface target detection method
  • Deep learning-based SAR remote sensing image water surface target detection method

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Embodiment Construction

[0021] The present invention is based on deep learning SAR image water surface target detection method, comprises steps as follows:

[0022] S1, collect SAR images, and expand the data set;

[0023] S2. Marking and labeling the SAR image data set to construct a training sample set. The labeling refers to recording the coordinates of the upper left corner point and the lower right corner point of the SAR image water surface target in a whole image, and the label is an index The category mark of the noted surface target;

[0024] S3. Design a convolutional neural network classification model C 0 , using the "transfer learning" method for C 0 Pre-training, design the RPN region proposal network model and the Fast R-CNN target detection network model based on the model;

[0025] S4, using the cross-training method to train the RPN region proposal network and the Fast R-CNN target detection network to obtain the final target detection model;

[0026] S5. Using the target detect...

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Abstract

The present invention discloses a deep learning-based SAR remote sensing image water surface target detection method, which mainly solves the problems of the slow detection speed and low positioning accuracy in the existing SAR image water surface detection method. The specific implementation is: designing and pre-training an image classification model; then designing and training a target detection model based on the model, wherein the target detection model comprises an RPN region suggestion network and a Fast R-CNN target detector; and finally using the trained model to detect the SAR imagewater level. The method disclosed by the present invention has the advantages of a high detection speed and high detection accuracy, and can be used for water surface target detection of large-area SAR images.

Description

technical field [0001] The invention belongs to the technical field of computer vision recognition, and in particular relates to a deep learning-based SAR image water surface target detection method in the technical field of synthetic aperture radar (SAR) image target detection. Background technique [0002] Synthetic aperture radar (SAR) has the characteristics of all-weather, all-time, high resolution and strong penetrating power, and is widely used in the fields of military reconnaissance and remote sensing. [0003] In recent years, with the rapid development of satellite remote sensing technology, sensor technology, computer technology, and communication technology, the application of space remote sensing technology has entered a new stage of development. Image object recognition and detection provides a large amount of data. As the main transport carrier and military target at sea, the ship's automatic detection and identification technology has broad application pros...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V10/255G06N3/045G06F18/214
Inventor 魏松杰袁秋壮蒋鹏飞罗娜
Owner NANJING UNIV OF SCI & TECH
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