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Polarized SAR image classification method based on extreme learning machine

A technology of extreme learning machine and classification method, which is applied in the field of ground object classification and target recognition, polarization synthetic aperture radar SAR image processing, and can solve the problems of complex algorithm model and long running time

Inactive Publication Date: 2015-12-30
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

Although this method can perform feature extraction and transformation to improve accuracy, the algorithm model is complex and the running time is too long

Method used

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  • Polarized SAR image classification method based on extreme learning machine
  • Polarized SAR image classification method based on extreme learning machine
  • Polarized SAR image classification method based on extreme learning machine

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Embodiment Construction

[0028] refer to figure 1 , the concrete steps that the present invention realizes are as follows:

[0029] Step 1, input the polarimetric SAR image to be classified and the filtered coherence matrix.

[0030] Input the marking information of the polarimetric SAR image to be classified, input a coherence matrix of the polarimetric SAR image to be classified with a size of 3×3×M, and use a Lee filter with a window size of 5×5 to filter out the coherent noise, and obtain the filtered The coherence matrix of , where each element in the filtered coherence matrix is ​​a 3×3 matrix, and M represents the total number of polarimetric SAR image pixels to be classified.

[0031] Step 2, obtain the data matrix according to the filtered coherence matrix.

[0032] The 3×3 matrix corresponding to each element in the filtered coherence matrix is ​​pulled into a 9-dimensional feature vector to obtain a data matrix with a size of 9×M.

[0033] Step 3, obtain the data set according to the dat...

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Abstract

The invention discloses a polarized SAR image classification method based on an extreme learning machine, mainly for solving the problem of low time efficiency of polarized SAR image classification. The method is realized by the following steps: inputting mark information of a polarized SAR image to be classified and a coherent matrix, and carrying out Lee filtering; extracting characteristics of the coherent matrix after the filtering, normalizing the characteristics to obtain a data set, and obtaining a mark set from the data set; dividing the mark set into a training set and a test set, and training the extreme learning machine by use of the training set; and predicating the type of the data set by use of the well trained extreme learning machine to obtain a polarized SAR image classification result. According to the invention, polarized SAR images are classified by use of the extreme learning machine, the classification precision is quite high, at the same time, the operation time is less, and the method can be applied to terrain classification and object identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to polarimetric synthetic aperture radar (SAR) image processing, which can be used for ground object classification and target recognition of polarimetric SAR images. Background technique [0002] Polarization SAR has all-weather and all-weather working ability, and its high resolution, can effectively identify camouflage and penetrate cover, so it is widely used in remote sensing and map surveying and other fields. In the past two decades, studies have shown that polarimetric SAR can provide more useful information than single-polarimetric SAR in target detection, object classification, parameter inversion, and terrain extraction applications. [0003] Traditional supervised polarization SAR image classification methods mainly include the following algorithms: Wishart supervised classification, which has low precision and low time efficiency; BPNN based on back...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/66
CPCG06V30/194G06F18/2411
Inventor 焦李成李玲玲曾杰马文萍张丹屈嵘侯彪王爽马晶晶尚荣华
Owner XIDIAN UNIV
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