Synthetic aperture radar remote sensing image ocean floating raft recognition method for deep-cooperative sparse coding network

A synthetic aperture radar and sparse coding technology, applied in character and pattern recognition, instruments, computer components, etc., can solve the problems that visible light remote sensing images cannot completely and accurately acquire breeding targets, single image features, and difficult target recognition

Inactive Publication Date: 2018-11-02
NATIONAL MARINE ENVIRONMENTAL MONITORING CENTRE
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Problems solved by technology

[0005] Aiming at the problem that the visible light remote sensing image in the existing technology cannot completely and accurately acquire the breeding target, an...

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  • Synthetic aperture radar remote sensing image ocean floating raft recognition method for deep-cooperative sparse coding network
  • Synthetic aperture radar remote sensing image ocean floating raft recognition method for deep-cooperative sparse coding network
  • Synthetic aperture radar remote sensing image ocean floating raft recognition method for deep-cooperative sparse coding network

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Embodiment Construction

[0022] The present invention will be further described below in conjunction with the accompanying drawings.

[0023]A method for identifying marine floating rafts in a synthetic aperture radar remote sensing image based on a deep collaborative sparse coding network, comprising the following steps:

[0024] The first step is to preprocess the SAR image to obtain a SAR image with accurate geographic coordinates, low coherent speckle noise, and high image visibility; the preprocessing mainly includes three parts: geometric correction processing, enhanced Lee filter processing, Gray scale stretch processing.

[0025] (1) Geometric correction processing to obtain precise geographic coordinates

[0026] Perform geometric correction processing on the SAR image, specifically: according to the image control point polynomial mode, select the second-order polynomial model to obtain the model parameters to obtain the correction model, and then determine the row and column values ​​of the...

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Abstract

The present invention provides a synthetic aperture radar remote sensing image ocean floating raft recognition method for a deep-cooperative sparse coding network, and belongs to the field of computerremote sensing image processing and pattern recognition. The method comprises the following steps: preprocessing a synthetic aperture radar remote sensing image to obtain a remote sensing image withan accurate geographic coordinate position, low speckle noise and high image visibility; extracting texture features and contour features of the radar remote sensing image; performing super-pixel segmentation on the texture image and the contour image; and finally, inputting a super-pixel block feature image into a classification recognizer to obtain a classification recognition result. The effectand benefit of the method provided by the present invention is that: the supervised classification accuracy of the synthetic aperture radar remote sensing image can be greatly improved.

Description

technical field [0001] The invention belongs to the field of computer synthetic aperture radar (SAR) remote sensing image processing and pattern recognition, and relates to a method for identifying marine floating rafts in synthetic aperture radar remote sensing images using a deep collaborative sparse coding network. Background technique [0002] The traditional clustering method only has a good clustering effect on the target sample data of spherical distribution. However, in the actual SAR image processing process, it is usually impossible to know the distribution type of floating raft culture in advance, so a supervised algorithm is used. [0003] According to the characteristics of SAR image remote sensing data, the supervised algorithm learns through training samples, so as to realize the classification of different types of targets. Zhang Hong et al proposed object-oriented high-resolution SAR image processing for marine target recognition and extraction (Zhang Hong, ...

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Application Information

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IPC IPC(8): G06K9/40G06K9/46G06K9/62
CPCG06V10/449G06V10/30G06F18/24
Inventor 范剑超赵建华张丰收耿杰胡园园王心哲
Owner NATIONAL MARINE ENVIRONMENTAL MONITORING CENTRE
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