Polarized SAR image convolutional neural network classification method based on spatial feature atlas
A convolutional neural network and spatial feature technology, applied in the field of convolutional neural network classification of polarimetric SAR images based on spatial characteristic maps, can solve shallow classification, insufficient feature extraction, low classification accuracy of polarimetric SAR images, etc. problem, to achieve the effect of improving the classification accuracy
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[0030] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings, but it is not limited to this. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention shall be covered by the present invention. In the scope of protection.
[0031] The invention provides a polarization SAR image classification method based on convolutional neural network, such as figure 1 As shown, the specific implementation steps are as follows:
[0032] Step 1: Input the polarized SAR image data and preprocess it to obtain the polarized SAR image to be classified.
[0033] Step 2: Determine the category of the polarization SAR image to be classified.
[0034] Step 3: Perform initial feature extraction of the polarization SAR image based on the scattering model and coherent decomposition of the polarization SAR image ...
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