Polarized SAR classification method based on deep learning of shallow-layer characteristics and T-matrix
A technology of deep learning and classification methods, applied in the field of image processing, can solve problems such as the impact of classification results, insufficient image expression, and increased workload of scientific researchers, and achieve good classification, accurate classification results, and rich information
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[0024] The embodiments and effects of the present invention will be described in further detail below in conjunction with the accompanying drawings.
[0025] Reference figure 1 , The implementation steps of the present invention are as follows:
[0026] Step 1. Filter the original polarized SAR image.
[0027] Input the polarized SAR image to be classified, use the refined polarized Lee filter in the polSARpro_v4.0 software, and remove the speckle noise in the image to be classified through a pixel sliding window of 7×7 to obtain the filtered polarized SAR image.
[0028] Step 2. Extract the polarized shallow features of the filtered polarized SAR image.
[0029] The existing common methods for extracting polarized shallow features include Freeman decomposition and Cloude decomposition. In this example, the Cloude decomposition method is used to extract the polarized shallow features from the filtered polarized SAR image. The steps are as follows:
[0030] (2a) The polarization coherenc...
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