Gaussian Processes Based Classification For Hyper-spectral Imagery

Hyperspectral imagery (HSI) is a three-dimensional imagery generated by imaging spectrometer simultaneously to the same surface scenery at hundreds of bands. It contains hundreds of spectral information in a narrow spectral region. One of the main applications of HSI is to identify and recognize the materials by the rich spectral information, which is also the basic reason for widely used of HSI to military and civilian fields.Currently, kernel methods are more and more popular in HSI classification for their ability to solve nonlinear problems and less sensitive to the curse of dimensionality with respect to traditional classification techniques. As a kind of kernel methods, support vector machine(SVM) classifiers are widely used for HSI classification in recent years. However, there are some drawbacks in SVM such as difficulty of hyperparameters selection and non probabilistic outputs,which prevent their further population.Another potentially interesting kernel-based classification approach is represented by Gaussian process classifier(GPC). By contrast to SVM classifiers, GPCs are Bayesian clas-sifiers and they permit a fully Bayesian treatment of considered classification problem. GPCs have the advantage of providing output probabilities rather than discriminant func-tion values. Moreover, they can use evidence for automatic model selection and Hyperpa-rameter optimization.In this paper,research is focused on Gaussian Processes(GP) based HSI classification. Aiming at HSI features such as numerous bands, highly correlations of spectral and spatial, lack of labeled samples, We combine Gaussian Processes with Affinity Propagation(AP), kernel construction methods, conditional random fields and semi supervised learning re-spectively to propose some new Gaussian Processes based methods.The major works and contribution of this dissertation are as follows:1. The dimensionality of HSI strongly affects the performance of many supervised clas- sification methods,which is called "Hughes" phenomenon. In order to avoid it, band selection should be processed before classification of HSI. Combined with Affin-ity propagation, Band selection based Gaussian processes method is proposed in this dissertation, which means band selection by AP is followed by classification by GPC. Experimental results show that the proposed band selection based Gaussian processes method can get better classification result in a few spectral bands.2. HSI shows strong spectral and spatial correlations. By constructing a new spatial kernel function (SGK) of GP, spatial relations in HSI are included, so that classifica-tion error partially caused by "same material different spectral" and "same spectral different material" can be partially eliminated.3. In order to utilize spatial structure of HSI, we make a combination of GPC with conditional random fields(CRF) and propose GPCRF method for HSI classification. Experiments on the real world Hyperspectral images attest to the accuracy and robust of GPCRF method, because it can reduce image noise to some extent.4. In HSI classification, supervised learning methods for classification often lead to low performance because of the hard of obtaining the labeled training samples. Mean-while, there are a lot of unlabeled data in Hyperspectral images. In semi supervised learning theory, labeled samples and abundant unlabeled samples are combined to train classifiers by estimating parameters of a generative model. A new classifica-tion method of Spatial Semi-supervised gaussian processes(SSGP) is proposed in this dissertation which is based on the assumption of semi supervised manifold as-sumption. SSGP is a semi-supervised learning method, and spatial correlations of labeled samples and unlabeled samples can be build to raise the classification cor-rect rate; SSGP is a kernel method and it can deal good with the nonlinear property of HSI; SSGP is a non-parameter method and has few Hyperparameters which can be learned from the data. Experiment results show that SSGP method is very good at classification of Hyperspectral images with respect to classification accuracy and stability at the case of small percentage of labeled training samples. In this dissertation, we take the advantages of HSI features such as abundant spectral bands, highly correlations of spectral and spatial and lack of labeled samples, improve stan-dard GPC and propose several GP based HSI classification methods. The results achieved show that these methods have the potential of yielding accurate and stability.Finally, we make a conclusion and give a research Perspective.