A hyperspectral image classification algorithm based on 3D convolution spectral space feature fusion
A hyperspectral image and feature fusion technology, applied in the field of hyperspectral image classification algorithms based on 3D convolutional spectral-space feature fusion, can solve problems such as inability to effectively deal with image noise, unsatisfactory accuracy, and incomplete information, and achieve classification accuracy The effect of promoting and improving training speed and high computing efficiency
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[0033] Example: such as figure 1 As shown, a hyperspectral image classification algorithm based on 3D convolutional spectral-spatial feature fusion, the algorithm is a four-layer structure, the first layer is to use 3D convolutional neural network to achieve spectral-spatial feature fusion, and its input data is A1 , the output is A2, the second and third layers use 1D convolutional neural network to extract features from the data, the input and output of the second layer are A2 and A3 respectively, and the input and output of the third layer are A3 and A3 respectively A4, the fourth layer is fully connected to the third layer, and the input and output are A4 and A5 respectively; it is characterized in that the hyperspectral image classification algorithm includes the following steps:
[0034] Step 1: Preprocess the data; use PCA whitening as a data preprocessing method to reduce the correlation of spectral information between different spectral channels, and obtain the data a...
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