Studies On The Methods And Application Of Multi-Sources Information Fusion In Remote Sensing

In recent years multi-sources data fusion techniques have already been an international research hotspot in Remote Sensing. Get useful information from Remote Sensing image and another information source of large areas is a time-consuming process. Computer-aided classification has provided an alternate, effective method. The main drawback of traditional remote sensing information computer classification methods is its low precision. Improving classification accuracy is a key issue in Remote Sensing image classification. Since multi-sources data fusion technique can efficiently improve the accuracy and the ability of fault tolerances, especially can deal with the problem of uncertainty and inaccuracy, it will play an important role in the multi-sources data analysis and process of Remote Sensing at present. According to the problem in the development of computer Remote Sensing image auto-classification, this paper does more detailed research in the area of feature and decision fusion classification.Based on unsupervised classification, improve the original Kononen Neural Network (KNN) by modifying learning rate and kernel function. And developed two fusion rules based on the magnitude of providing recognition information of each information sources. One is weighted; another is not weighted. Experimental results show that it can obtain better classification, as well as the recognition results of mix pixel are improved at some degree.Although fusion method based on KNN can deal with multi-sources Remote Sensing information, results show that a fast fusion classification method for a great number of image and another information sources has not yet devised. Though some detailed analysis of Remote Sensing information, this paper developed a method of fusion classification, which improves accuracy and convergence, by integrated fuzzy technique and KNN.Since structuring a right membership function, that is also effectively extracted feature, this paper proposed two modified method MFKNN1 and MFKNN2. In addition, in order to guarantee the smoothness of weight vector distribution in output space, MFKNN2 method is introduced a smooth factor in the modification of connective weight coefficient between neuron. Simulative experimental result suggested that these two methods are effective in improving classification accuracy and capability of algorithm.By integrated fuzzy logic, neural network and genetic algorithm, a fusion algorithm ofevolution programming FKNN is presented. Based on learning sample, this method can automatically determined the node of output space and can find a global minimum. So EPFKNN method results in accuracy and quickness. For classifying mix-pixel, it shows that the capability is better.Another emphasis is the fusion method research of decision level in this thesis. In fact, two methods are researched. One is the Basic Dempster-Shafer evidence theory (BDSET); another is the fuzzy Dempster-Shafer evidence theory (FDSET). In evidence theory, it is an important that how to obtain Basic Probability Assignment (BPA) function. According to the classification theory of Remote Sensing image, BPA function and the rule of fusion classification are determined respectively. The experimental results show that these two classification methods of multi-sources information fusion can result in better accuracy than that of conventional unsupervised classification method.Discuss the method and technique about integrated multiple levels data fusion based on the characteristics of multi-sources Remote Sensing information fusion classification. A classification frame of multi-sources Remote Sensing information fusion is designed by using FKNN of feature level fusion and FDSET of decision level fusion. Based on the multi-sources Remote Sensing information available, the frame of classification can be used computer-aided automatic classification by training. The classification accuracy is distinctively superior to conventional classification method.In general, some theory discussion and method r...