Polarized SAR image classification method based deep multi-example learning

A multi-instance learning and classification method technology, applied in the field of polarimetric SAR image classification based on deep multi-instance learning, can solve the problem of low image classification accuracy, and achieve the effects of improving classification accuracy, improving feature information, and enriching feature information.

Active Publication Date: 2016-08-24
XIDIAN UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to overcome the above-mentioned defects in the prior art, and propose a polarimetric SAR image classification method based on deep multi-instance learning, which is used to solve the pr

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  • Polarized SAR image classification method based deep multi-example learning
  • Polarized SAR image classification method based deep multi-example learning
  • Polarized SAR image classification method based deep multi-example learning

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[0028] Reference figure 1 The specific implementation steps of the present invention are as follows:

[0029] Step 1. Input the polarization SAR image to be classified, and perform refined polarization Lee filtering on the image to obtain the polarization SAR image containing the polarization coherence matrix T and the polarization covariance matrix C, which is achieved through the following steps:

[0030] Step 1a: Set the filter window size of the SAR image to be polarized to obtain multiple mean windows, select edge templates in different directions for each mean window, and perform edge detection to obtain multiple directional filter windows: classify the polarized SAR image to be classified On the span total power image, the value of each pixel of the image is equal to the sum of the diagonal elements of the polarization covariance matrix of the pixel, and the size is set to 7×7 filter window, according to the image pixel spatial position, from left to The filter window is dec...

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Abstract

The invention discloses a polarized SAR image classification method based deep multi-example learning, and solves the technical problem of low classification precision due to insufficient feature extraction in a conventional polarized SAR image classification method. The method includes the steps of filtering polarized SAR images, selecting a training sample set, extracting sample characteristics, initializing a convolutional neural network (CNN) and a deep belief network (DBN), normalizing sample characteristics, training the CNN and the BDN, extracting combined characteristics, inputting the combined characteristics to an SVM classifier for training, classifying polarized SAR images through the trained SVM classifier, outputting the classification result and calculating classification precision. The method effectively combines image spatial neighborhood characteristics and polarized characteristics, thereby improving the classification precision for polarized SAR images. The method is applicable to terrain classification and target identification for polarized SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a polarization SAR image classification method, in particular to a polarization SAR image classification method based on deep multi-instance learning, which can be used for ground object classification and target recognition of polarization SAR images. Background technique [0002] SAR is a synthetic aperture radar, which has the advantages of all-weather, all-time, high resolution, and side-view imaging. Polarization SAR is a high-resolution active coherent multi-channel synthetic aperture radar, which is an important part of SAR. It has the characteristics of acquiring data through multi-polarization channels, and can express more abundant information than SAR. It can be widely used in military, navigation, agriculture, geographical surveillance and many other fields. It is extremely important in the field of international remote sensing, so polarimetric SAR image class...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 焦李成刘旭张丹赵佳琦赵进尚荣华侯彪杨淑媛马文萍马晶晶
Owner XIDIAN UNIV
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