High Resolution Uav Remote Sensing Image Segmentation Based On Texture Information

The contradictions between the increasing need of Remote Sensing Image Data and Outdated image segmentation technology is becoming increasingly evident, altHough the amount of High-Resolution Remote Sensing data growing exponentially in recent years. The outmoded Image Analysis technologies can not convert Raw Remote Sensing Image Data to be useful data for application in Engineering is tHough to be the major reason. Image segmentation is a key step in image processing and image analysis. So, it is necessary to renew the knowledge of Relevant Technologies and make improve, which include texture analysis and image segmentation algorithm in this thesis.Study on Object-oriented Segmentation algorithm of Remote Sensing Image. Based on the analysis of the edge detection and Seeded Region Growing algorithm, a new algorithm was brought up, which is different from classical algorithm in two aspects:First, a new criterion of region growing is designed. Secondly, we propose a region merging rules based on the area and contrast, which can restrain the over-segmentation problem effectively and improve the anti-noise ability. Experiment results indicate that the method can improve the quality of image segmentation.Study on texture information extraction technologies. The Comparison of the two kind of Texture information extraction technology was carried out. Through the study on the correlation of the 11 texture feature and the effect of the window size on texture features quality, a scientific basis for choosing texture feature and the optimum window size was presented.According to the Research above, a texture image segmentation algorithm based on object-oriented which combines texture features and spectral characteristics was presented. In order to verify the accuracy of image segmentation, the new algorithm was applied to segmenting UAVRS images and a comparison between this approach and classical classification approaches has been carried out. The study conclusion shows that the new method is and more reliable comprehensive in reflecting the ground objects in the image.