The Study Of Semi-supervised Classification Technology In Remote Sensing Image Processing

The classification method based on statistical pattern recognition is one of the most important methods in remote sensing image information extraction field. Because of the high degree of complexity and randomness on the statistical distribution which remote sensing information holds, as well as the operator's limited knowledge of the images and the blind choice of training samples, the number of samples which we obtained usually uninsufficient and had poor representativeness. We cannot image that a good classification result can be obtained with these samples. But people often ignored this problem both in classification methods research and application, which is not conducive to remote sensing image classification and information extraction technology research development. This thesis mainly aims at dealing with the poor sample representativeness problem and studying how to mining the potentialities of the classification methods and the classified images, so that the classification results can be improved.The thesis begins with an overview of the basic theories and recent research on semi-supervised learning field and remote sensed image classification field. The bulk of author's contribution, which form the main part of this thesis, are the semi-supervised classification methods developing for remote sensed images, and its application to dealing with two important problems in remote sensing application field.(1) Semi-supervised classification methods development for remote senseing imagesIn analyzing the characteristics of remote sensing image data, based on the two kinds of assumptions which the semi-supervised learning and remote sensing image data distribution share, the methods based on generative model and transductive model of semi-supervised learning for remote sensing image classification are proposed and developed.In generative model-based method studying, we re-derivate the EM algorithm and amendment a recursive formula, and give the reason why we do so. According to the characteristics and needs of remote sensing image classification, a one class corresponds to one component and a one class corresponds to more components models are given out, and the corresponding algorithm flows are developed respectively. We also find out that the Hughes phenomenon exists in semi-supervised learning category too. Based on the designed classification experiments, we give our suggestion that the usage ratio of unlabeled and labeled samples must not be too large or small.In transductive-based methods studying, we propose a new unlabelled sample labeling method according to the remote sensing data and application characteristics, then apply it to the current mainly used medium-low resolution remote sensing images and high-resolution remote sensing image classification. At last we give out the pixel-based and object-based semi-supervised classification process which has great difference with the traditional pixel-based classification method.In the end of this part, we compare the generative model method and the transductive model method by apply them to the same image classification.(2) Automatic classification and training samples space-time expanding methods development for remote sesing image based on the proposed semi-supervised methodsAutomatic classification is the important direction of technology development for large-scale, high frequency, repetitive regional remote sensing monitoring in the future. We propose a semi-supervised remotely sensed image classification method in the way of establishing training samples data sets. Here labeled samples are no longer come from the image to be classified itself, but to some extent on behalf of the image area covered. For the high demand of automatic information extraction from remote sensed images in state sea used monitoring, we proposed a coastal zone usage information automatic extraction method based on the semi-supervised classification method we proposed in the first part.Remote sensing image classification sample space-time expansion plays an important role in the emergency disaster monitoring, cross-regional and cross-border monitoring and other aspects of military. Poor representative is the main problem for training samples applied in the images which cover different space and time. We proposed a semi-supervised based method to deal with this problem. Labeled samples are completely not from the image to be classified and even cannot cover the place the image covers. The classification experiments on regional expanding and border expanding are carried out, and the result shows that our method works well.In the end, the methods which we proposed to dealing with the poor representative of man-picked training samples problem are summarized and discussed, and further research directions and goals are given out.