| Summary: | 碩士 === 逢甲大學 === 土地管理所 === 92 === In the research of land-use information survey and monitoring broad and simultaneously, traditional land surveying methods, such as field surveys, are time-consuming and costly. In the past, per-pixel classification algorithms were used for research, which did not consider about the location of pixel within the image and the relationship between its spectral response and neighborhoods. So, it can’t classify land use categories accurately.
Urban areas comprise a complex spatial assemblage of land-cover types, each of which may have different spectral reflectance characteristics.If we use pixel-based classification Algorithm, we will get poor classification accuracy. In the process of classification of remote sensing imagery, image segmentation play very important role. For overcoming the above problem of spectral classification, we can get landuse information by utilizing image segmentation,.
So, in this research on classification of land use categories from high resolution satellite images, we will use a segmentation method to improve image classification accuracy and make up the shortage on the other methods. In this method, on the one hand we considered three factor of gray level, pixel size, and the relationship between neighbor images, and on the other hand we use two automatic threshold selection for segmentation.Therefore, in this research, we utilized image segmentation to improve the classification results of land use categories from QuickBird satellite images. Then, we composed the program to extract the information in the image space for the application of practice. Finally, we obtained good image classification accuracy (overall = 84 percent; Kappa coefficient = 0.81) by using degree of fitting threshold selection for segmentation in QuickBird satellite images and segmented image automatically.
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