Research On The Hyperspectral Imagery Classification Based On The Neural Network

The enhancement of spectral resolution is the trend in development of the optical remote sensing. High spectral resolution (hyperspectral for short) remote sensing is one of the significant technological breakthroughs for the observation to ground in the past 20 years, and is the advancing front technology of the current remote sensing. The hyperspectral's latent application receives widespread attention because of its high spectral resolution.The research of hyperspectral imagery classification is one of the main contents of the hyperspectral remote sensing application. The neural network is an integrated data classification method which is developed in recent years. The neural network has some merits when it is used to the hyperspectral imagery classification. First, it doesn't require the same culture distributing as normal, and doesn't need to do the probability distributional assumption to the primitive classification, not existing the problem of solving the probability distribution parameter. Second, it can make kinds of data, such as texture information and topographical information and spectral information, fused conveniently and effectively for classification.Because of the above merits, the research on the hyperspectral imagery classification by the neural network is very necessary. The purpose of this thesis is to carry on research to the structure of common neural network such as BP network, RBF network, training algorithm, and combine other theory knowledge to look for the valid method of hyperspectral imagery classification. This thesis mainly finishes the following work:First, the standard BP algorithm based on the error gradient descent is too slow for most practical application, to these insufficient, people have already proposed a lot of improvement schemes of standard BP algorithm. The thesis introduces the basic principles of several kinds of improvement BP algorithms, and sums up and compares the performance of various algorithms when using them to the hyperspectral imagery classification.Second, the choice of initial weights of BP network lacking theory guidance become a drawback of BP network application, and it will make the performance of BP network receive serious influence that initial weights are chosen improperly. For this reason, this thesis combines BP network with the decision fusion theory to improve the accuracy of the hyperspectral imagery classification.Third, the main parameters that affect the classification accuracy of the RBF network are: the number of the hidden neurons, the widths and centers position of kernel function of the hidden neurons, the weights between hidden-layer and output-layer and the biases of the output neurons. This paper proposes a new method to design the five parameters according to the characteristic of the hyperspectral image.Forth, the situation of commaterial with different spectrum often appears in the hyperspectral imagery classification. The thesis uses the neural network based on target decomposition to the classification. The classification method of neural network based on target decomposition is made up of three parts, the target decomposition, the neural network classifier and the subclass merged. It divides the same class with different spectrum into several kinds of subclasses. The spectrum of each subclass is identical and unimodal normal distribution. Then the subclasses decomposed are regarded as different ground objects and sent into the neural network to train. Finally, a logic operation is added when the network exports to make the subclasses merge into the original classification again, so as to improve the accuracy of hyperspectral imagery classification.Fifth, the hyperspectral image contains a large amount of mixing pixels because of the spatial resolution and complicated variety on the ground. The problem of resolving the mixing pixel effectively is an outstanding question of the hyperspectral application. This paper uses a method of variance purifying samples to extract endmember, and uses the RBF network to unmix the whole hyperspectral image.