Study On The Hyperspectral Remote Sensing Image Classification Based On Deep Generative Adversarial Model

Hyperspectral image,which is an important data source in remote sensing area,contains both spectral and spatial information of the scene.Therefore,it is widely utilized in the field of precision classification,quantitative retrieval and target detection etc..Classification is the most primary problem of hyperspectral image analysis.There is a wealth of information in hyperspectral image.However,the issues of large data redundancy,low spatial resolution and small training set are still the barrier of the application and development of hyperspectral image.In view of the above issues,this dissertation introduced the deep generation model which is with remarkable performance in computer vision task,and carried out researches in below aspects:generation of hyperspectral image,stable training process of deep generative model and spatio-spectral feature learning.The contributions of this dissertation are summarized as follows:(1)An innovative generative network named conditional variational and adversarial autoencoder(CVA~2E)has been proposed.Firstly,the causes of training instability,model collapse and low quality of generated samples are analyzed.Then variational inference process is introduced into the generative adversarial network to improve the generative ability of the model.Moreover,to learn the fine-grained spectral characteristics of individual hyperspectral pixels,the spectral angle distance and vectorial angle measurement are introduced in the loss function of CVA~2E.The improved CVA~2E shows a superior performance in the spectral synthesis of different categories.To demonstrate the ability of the generated samples for the classification task,three kind of scenarios,ROSIS,AVIRIS and HYSPEX dataset,were carried out.Experimental results showed that the proposed model achieved the best performance.CVA~2E with spectral angle distance and vectorial angle measurement obtained the best results of 96.74%and 89.7%on the ROSIS dataset and AVIRIS dataset,respectively.Besides,CVA~2E with vectorial angle measurement obtained the best result of 98.33%on the HySpex dataset.(2)An innovative network named capsule triple generative adversarial network(Caps-TripleGAN)has been proposed.Firstly,CVA~2E is rediscussed to explore the reason of its weak-coupling training process.In this regard,TripleGAN is introduced which consists of classifier,generator and discriminator.Moreover,CapsNet is modified to adapt to the hyperspectral imagery classification,which takes the features and their locations,as well as their directions,into account during feature extraction process.To demonstrate the ability of the generated samples for the classification task,three kind of scenarios,ROSIS,AVIRIS and AHSI,are carried out.It has found that the CapsNet outperforms other deep learning approaches and TripleGAN can improve the performance of CapsNet on small training dataset.(3)Spatio-spectral features have been explored and two innovative methods have been proposed based on deep generative model.Firstly,based on the tight-coupling networks proposed in Chapter 4,the spatial information and spectral features are inputted to the network separately.After that,the extracted features are combined in the deep layer to construct the dual channel TripleGAN(Dual-TripleGAN).The other proposed approach is named multiscale generative assistant capsule network(MS-GA-CapsNet).During the learning process of spatio-spectral blocks,pixels in the blocks are reweighted using different weights,and the spectrum of the central pixel is generated in a mixed condition by taking the label information of the neighborhood into account.In the process of spatio-spectral feature extraction,multi-scale CapsNet is utilized.To demonstrate the ability of the proposed methods,three datasets ROSIS,AVIRIS and HYSPEX which from airborne imaging spectrometer and AHSI which from spaceborne imaging spectrometer were utilized.The results showed that the MS-GA-CapsNet achieved the best performance.There are 71 figures,27 tables,and 170 references in this dissertation.