Hyperspectral image intelligent unmixing method based on unsupervised training
A hyperspectral image, unsupervised technology, applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve problems such as the reduction of spatial accuracy and affect the application of hyperspectral data, and achieve the effect of excellent unmixing accuracy
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[0041] The present invention will be further described below by means of the accompanying drawings and specific embodiments.
[0042] Step 1: Build a deep neural network stacked by three autoencoders.
[0043] (1) Build the first autoencoder
[0044] The autoencoder includes two parts: encoding and decoding. The input of the first autoencoder is the hyperspectral matrix X, and the sigmoid activation function is used to construct the encoder according to the encoding formula (1) and decoding formula (2). l=1 means the first self-encoder, a l is the encoded output value of the first autoencoder, is the decoded output value of the first autoencoder, and are the weights of the encoding layer and decoding layer of the autoencoder respectively, set (*) T means transpose, Random numbers are used for initial assignment. The basic formula of autoencoder network is as follows:
[0045] alpha 1 = sigmoid(w e1 *X) (1)
[0046]
[0047] (2) Batch normalization before th...
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