SAR image classification method based on hierarchical sparse filtering convolutional neural network

A convolutional neural network and sparse filtering technology, applied in the field of image processing, can solve the problems of limited application, low classification accuracy, difficult to overcome the influence of noise, etc., to achieve the effect of overcoming coherent speckle noise, high classification accuracy, and stable classification results

Active Publication Date: 2015-12-09
XIDIAN UNIV
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Problems solved by technology

These factors limit the application of traditional image classification techniques such as template matching, model-based and kernel-based classification techniques in SAR image classification
At present, there are three main problems in SAR image recognition technology that need to be solved urgently: (1) Due to the large amount of coherent speckle noise in SAR images, it is difficult to overcome the influence of noise by using common feature extraction methods, and the classification accurac

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  • SAR image classification method based on hierarchical sparse filtering convolutional neural network
  • SAR image classification method based on hierarchical sparse filtering convolutional neural network
  • SAR image classification method based on hierarchical sparse filtering convolutional neural network

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[0032] Reference figure 1 The implementation steps of the present invention are as follows.

[0033] Step 1: Divide the SAR image database sample set into a training data set x and a test sample set y.

[0034] First, from each sample set containing 6 types of SAR image database sample sets, 1000 pictures with a size of 256×256 are taken, and then 200 pictures are randomly selected from each type of pictures to form the training set x, and the rest are used as the test set y.

[0035] Step 2: Randomly extract training image blocks of m block size d×d from the training data set x, and perform global contrast normalization to form a training image block set

[0036] Step 3: Use the training image patch set X to train the first layer of sparse dictionary.

[0037] 3a) Express the feature matrix of the training image block set X as:

[0038] F = ( X D ) 2 + ϵ ,

[0039] among them Represents a dictionary, N represents the number of features of each image bl...

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Abstract

The invention discloses an SAR image classification method based on a hierarchical sparse filtering convolutional neural network. The SAR image classification method comprises the steps of 1. dividing an SAR database sample set to a training data set and a testing sample set; 2. studying a first-layer sparse dictionary from the training data set; 3. extracting a first-layer sparse characteristic chart by means of the first-layer sparse dictionary and performing nonlinear transformation, studying a second-layer sparse dictionary from the first-layer sparse characteristic chart; 4. extracting a second-layer sparse characteristic chart by means of the second-layer sparse dictionary and performing nonlinear transformation; 5. cascading the first-layer nonlinear transformation characteristic and the second-layer nonlinear transformation characteristic and training an SVM classifier; and 6. extracting the sparse characteristic of a testing set by means of the first-layer sparse dictionary and the second-layer sparse dictionary, and performing classification by means of the SVM classifier. The SAR image classification method settles the problems of high complexity, low universality, low noise resistance and low classification precision in prior art. Furthermore the SAR image classification method can be used for target recognition.

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Application Information

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Owner XIDIAN UNIV
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