SAR image classification based on 2D-PCA and convolution neural network
A convolutional neural network and 2D-PCA technology, applied in the field of image processing and SAR image classification, can solve the problems of reduced classification accuracy, high noise sensitivity, poor model robustness, etc., to reduce dimensions, improve classification accuracy, The effect of reducing complexity
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[0026] The realization scheme of the present invention is: select the training image and the test image and carry out amplification, use two-dimensional principal component analysis to reduce the dimensionality of the data after amplification, build the convolutional neural network model, use the training sample after dimensionality reduction to train convolution The neural network model finally classifies the test samples.
[0027] refer to figure 1 , the specific implementation steps of the present invention are as follows:
[0028] Step 1. Select training and test samples and perform amplification.
[0029] 1.1) From the MSTAR data set containing BMP2 armored vehicles, BTR70 armored vehicles, and T72 main battle tank data, a total of 1617 images with a depression angle of 17 degrees are selected as training images X 1 , each training image is randomly translated by (x, y) pixels in the horizontal and vertical directions, where x, y are integers within (-10, 10), repeating...
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