LiDAR結(jié)合高分辨率影像的城市不透水地表提取研究
[Abstract]:The large-scale impermeable surface expansion brought about by urbanization has an important impact on the urban ecological environment, which makes the original water permeable and good natural resources become non permeable construction land. The increase of urban water permeable surface will aggravate the pollution of urban water resources and reduce the vegetation, and make the city face serious ecological environment problems. Urban water permeable surface is not only an important indicator to measure the degree of urbanization, but also one of the key technical indicators to measure urban environmental change and social and economic development. Accurate and effective extraction of urban water permeable surface information has a certain guiding role for urban sustainable development and planning. It is one of the hot and difficult points in the field of remote sensing to classify the detailed objects and extract the impermeable surface. In this paper, a development area in Hengyang, Hunan province is taken as the research area, and the airborne LiDAR data combined with high resolution image data is combined together, and the object oriented method is adopted. The classification results of the unpermeable surface information of the urban area are extracted and used in the classification of the single image data and the multi source data, and the comparative evaluation and analysis are carried out to complete the detailed classification of the urban land types with high precision. The fine classification and extraction of the information of the urban water permeable surface is realized, and the high precision and the water surface is not permeable to the surface. Remote sensing estimation provides a new idea and method. The innovation of this paper is: combining LiDAR and high resolution aerial images to complete the detailed classification of urban ground information. Under the limited conditions of RGB image, a new algorithm is used to complete the extraction of the type of the surface related to the surface of the impervious surface; combined with LiDAR and high resolution aviation The main conclusions of this paper are as follows: (1) the main conclusions are as follows: (1) the object oriented analysis technology is introduced, and the image segmentation algorithm and fuzzy mathematical classification are discussed in detail. Methods. The theory and method of multi scale segmentation algorithm are summarized, and the selected methods of the segmentation parameters are discussed in detail. Secondly, the fuzzy classification theory is introduced, and the fuzzy classification rules and classification systems are expounded on this basis. (2) the high resolution aerial images of R, G and B in the research area are constructed. Supervised classification workflow and classification system. Multi-scale segmentation is the key of object oriented technology and classification premise, so this paper divides the image into multi scale before all the classification steps, and finds the segmentation parameters suitable for various types of city objects. Finally, according to the supervised classification rules and methods, the detailed classification of the urban area is obtained. The results and impermeable surface. (3) establish a set of technical process, classification system and rules for urban water permeable surface information extraction, integrated LiDAR data and two kinds of data sources obtained by the same machine to extract the water surface of the urban area, and use the fuzzy classification method to create a method for extracting the water surface of the city. The results are evaluated and analyzed by the best classification results and the obfuscation matrix accuracy verification method. The results show that the method can obtain more satisfactory surface information of the complex terrain in urban areas. (4) the multi source data coordination method can improve the image classification accuracy. The two forms are combined with the single aerial image and the image and LiDAR data. Taking the urban impervious surface and evaluating the accuracy of the classification results of the two, it is found that the multi source data can be optimized by the advantages of the data in the extraction of the impervious surface and make the classification results optimized. At the same time, using the method of combining the characteristics of the two data, the detailed and appropriate points are put forward for the special objects such as the bare land, such as the bare land. Class method and feature reference, and get more accurate classification results.
【學(xué)位授予單位】:新疆大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2017
【分類號(hào)】:P237
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