A hyperspectral unknown target detection method based on evm and deep learning
A technology of target detection and deep learning, applied in the field of hyperspectral unknown target detection, to achieve the effects of wide application range, high real-time performance, and simple operation
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[0038] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation examples.
[0039] figure 1 The overall flow chart of the hyperspectral unknown target detection algorithm is shown. First, the hyperspectral image data is preprocessed and the data set is divided, then the test sample data is input into the 3DCNN network for training, and the training model is saved and the 3DCNN model is saved. The output of the last fully connected layer is used as the feature vector of the corresponding sample. The output feature vector obtains the weibull probability model corresponding to each sample through the EVM algorithm, and obtains the final EVM model composed of the feature vector corresponding to the sample, the label and the weibull probability model by reducing the model. Finally, the preprocessed test sample data is input into the 3DCNN model and the EVM model, and the probability of belonging to a kn...
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