Hyperspectral Image Unmixing Based On Convolution Neural Network

In recent years,due to the development of imaging technology,hyperspectral unmixing technology has been widely used in more and more fields.Before the emergence of hyperspectral unmixing technology,hyperspectral images were all studied by "hard classification" method,which forced the mixed pixels into a certain category,which was obviously inaccurate,thus producing the "soft classification" of hyperspectral images,namely,the unmixing of hyperspectral images.In recent years,due to the rapid development of deep learning,neural network framework has been widely used in various fields,and good results have been achieved.Since the essence of hyperspectral unmixing is a function approximation problem,and neural network can infinitely approximate any continuous function,the research content of this paper is mainly to apply neural network in hyperspectral unmixing algorithm,and the main content is as follows:1.Since hyperspectral unmixing is an approximation problem,and neural network can fit any function,in addition,hyperspectral images have similar abundance in adjacent pixels in space,so this paper proposes hyperspectral unmixing algorithm based on spatial spectral similarity convolutional neural network(CNN).For a hyperspectral pixel,the algorithm takes the corresponding abundance as a label and approximates the abundance through convolutional neural network to optimize the whole network and improve the performance of mixing.In this method,the corresponding hyperspectral data are obtained by generating random abundance.Then the hyperspectral image is preprocessed and the weight of adjacent pixels is set by calculating the spectral angular distance of adjacent pixels and the position relative to the center pixel.Hyperspectral data are input into the convolutional neural network as training samples,and the back propagation algorithm is used to train the network,through which the hyperspectral data are unmixed.Experimental results show that the performance of this algorithm is better than that of SUnSAL,MLP and convolutional neural network.2.Aiming at the randomness of training samples,a hyperspectral unmixing algorithm based on improved Generative Adversarial Network(GAN)is proposed.Firstly,the hyperspectral data are unmixed by SUnSAL method to obtain the initial abundance.Then,input random noise to generate pseudo-data in the Generative Adversarial Network(GAN),input the pseudo-data and the abundance obtained in the previous step into the discriminator to judge the true and false,train the whole counter network,and finally get the abundance similar to the initial abundance.By calculating spectral angular distance(SAD)and correlation coefficient,it can be known that,compared with random samples,GAN samples are more similar to the original samples.The abundance generated by the generator is linearly multiplied by the end element matrix to obtain the hyperspectral data,which is used as the training data.The training data is input into the convolutional neural network,and the back propagation algorithm is used to train the network,and hyperspectral data is input.The network approaches its corresponding abundance through training,and the hyperspectral data is unmixed through the trained network.Experimental results show that the performance of this algorithm is better than that of SUnSAL,MLP and convolutional neural network.3.Aiming at the problem that hyperspectral data usually has hundreds of bands and there is a large amount of data redundancy,which will cause slow calculation speed and occupy a large number of channels for data storage,a band selection method based on rmse index for hyperspectral unmixing is proposed.Firstly,input hyperspectral images are arranged in descending order according to the best combination index(OIF),and then the errors of subsets of adjacent bands are calculated.Finally,threshold value is set to remove redundant bands in hyperspectral images according to rmse index,so as to complete the band selection of hyperspectral images.The experimental results show that the band selection method for hyperspectral unmixing proposed in this paper is obviously superior to the general band selection method,and improves the operation speed of the mixing algorithm,which is more suitable for hyperspectral unmixing.