Hyperspectral unmixing network considering spectral information and superpixel segmentation
The pixel is the basic unit of remote sensing images.If a single pixel contains multiple types of covering ground objects,then it is called a mixed pixel.Hyperspectral unmixing aims to decompose the mixed pixels into several basic component units(endmembers)and obtain the proportion(abundance)of each endmember,which can improve the accuracy of remote sensing image classification and subpixel level target detection.Thus,research on this method promotes the development of hyperspectral remote sensing technology.Studies show that considering the spatial information in the process of hyperspectral unmixing can effectively improve the unmixing accuracy.However,most of the nonlinear unmixing networks based on deep learning only use the spectral information of images.A hyperspectral unmixing network considering spectral information and superpixel segmentation(SSUNet)is proposed on the basis of the supervised unmixing idea and one-dimensional convolutional neural network to maximize the spectral and spatial information of images.First,the original hyperspectral data should be processed using superpixel segmentation to obtain the superpixel segmentation data with spatial characteristics.Then,SSUNet is used to train and unmix the original hyperspectral and superpixel segmentation data.The loss function adds regularization constraint term based on the root mean square error to promote the sparsity of the unmixing abundance and generate closer unmixing results to the real value.The activation function of the network output layer is softmax,which yields output values of each output node within the range of[0,1]and constrains their sum to 1,thus satisfying the two constraints of unmixing:the abundance nonnegative constraint and abundance sum-to-one constraint.Experiments on simulated datasets generated by the linear and nonlinear mixed models and the two real datasets show that the proposed network has higher unmixing accuracy and better robustness than the unmixing results of SUnSAL,SUnSAL-TV,SCLRSU,MTAEU,EGU-Net-pw,and 1DCNN.Three Gaussian noises with different SNR levels(20,30,and 40 dB)are added to the simulated dataset.The proposed network can achieve the best unmixing results at all SNR levels,and the network also achieves high unmixing accuracy with the increase in SNR.In addition,the influence of the change of w value on the unmixing result of the simulated datasets under different SNR is verified.The experimental results show that when the value range of w is[3,13],the RMSE value does not change substantially,and the best value of w is 5.Experiments on real datasets show that SSUNet can still achieve the best unmixing results in complex real scenes.The SSUNet network uses the dual-branch structure to mine the features of the original image data and the superpixel segmentation data with spatial features.This network also utilizes the fusion layer to fuse the features and improve the unmixing accuracy of the model.Experiments on simulated and real hyperspectral datasets show that the proposed network has high accuracy.
hyperspectral imageshyperspectral unmixingspectral and spatial informationsuperpixel segmentationdeep learningconvolutional neural network
万方数据
维普
