Research On Segmentation And Extraction In Optical Remote Sensing Image

Segmentation is plays a vital role in remote sensing image processing. In the meanwhile, optical remote sensing image plays an important role in both military and civil situations. Therefore it is of great value about the research on optical remote sensing image segmentation. Existing remote objects extraction research mostly focuses on some kind of special objects in infrared images or SAR images. But the segmentation of optical remote sensing image mostly focuses on surface features landscape classification of low-middle spatial resolution optical remote sensing images. Taking the surface features landscape classification of low-middle spatial resolution optical remote sensing images as segmentation, existing methods mostly cannot get satisfied efficiency and reliability. For the surface objects with clear border and high-middle spatial resolution, the existing extraction methods are mostly depends on human experience or semi-automatic human-computer interaction. So the intelligent and automatic methods are necessary. In this paper, we study some key issues on objects segmentation and extraction in optical remote sensing images. Based on different segmentation requirements, for the low-middle spatial regional objects or high-middle small objects, different segmentation or extraction methods are proposed here. Experiment result on the actual remote sensing images gives evidence of this method's efficiency.In this dissertation, based on the existing segmentation research of the remote sensing image processing; our work are as follows:Firstly, low-middle spatial resolution optical remote sensing image is taken into account. We take urban as an example of area objects and focus on the segmentation. The urban objects usually have a fuzzy boundary and Connectivity large area, so some segmentation speed is very slow. In this dissertation, a vectorized fuzzy segmentation method is proposed based on fuzzy set theory. The fuzzy membership function is constructed based on the model method and the fuzzy statistical method. The effectiveness of our method is verified by a fuzzy training process. Experiment result shows more efficient and higher accuracy compared with the traditional method.Secondly, we consider the segmentation and extraction in middle-high resolution optical remote sensing images. The existing segmentation is either the supervised automatic segmentation with priori knowledge or the unsupervised segmentation with human initialization. A complete automization method is proposed, without initialization or human interaction. It is a data loss problem in computer vision. We do some work with the initialization of expectation maximization and propose a direction labeling method, which is used in the iterations of parameter estimization process. Experiment result shows the efficiency and reliability.Thirdly, shape prior is introduced when the object of interest is in complicated background, such as ship objects under cloud in sea background. A cloud cover ship model is proposed based on features of optical remote sensing images. Based on the prior of the ship's shape template, the model is constructed by putting the object and complex background together. Then the energy functions are constructed, and ship extraction is completed until the corresponding energy has been minimized. Experiment result gives evidence of this model's efficiency.Finally, in order to get an object interpretation in the whole optical remote sensing image, we propose a human interactive interpretation system for objects of interest, which assures high accuracy and low time complexity at the same time. Our contributions are as follows:A vectorization fuzzy segmentation method for urban area is proposed based on the Bayes Rule. Compared with traditional multi-scale segmentation, our method is more effective and more accurate.A direction labeling method with automatic initialization is proposed for the parameter estimation of the vision. The object segmentation process is realized with its own initialization and iteration. The process only needs the spectrum and color attributes of the image, without initialization and human interaction. Experiment result shows the efficiency and reliability.A cloud over ship model is proposed for the ship objects under cloud in sea background. And the model is constructed by putting the object and complex background together. Different energy functions are constructed according to whether there is a cloud shadow. Experiment result shows the efficiency.In this dissertation, some research on theory analysis is presented at the beginning, and then some corresponding algorithms are constructed. Experiment result shows the efficiency and reliability of these algorithms.