The Study On Automatic Extraction Area Feature From Remote Sensing Image

With the development of remote sensing technology, Massive multi-time remote sensing images have been accumulated through being observed all year round and information being shared in related organizations. A wealth and rich of information can been interpreted from remote sensing images used some technical means. But now the speed of extracting information from the remote sensing images can not keep up with the speed of getting the remote sensing images. The capacity and efficiency of information extraction has become a bottleneck in the development of remote sensing applications. So far, in order to obtain more precise information, people have to use the artificial or human-computer interaction to extract information mostly. Now the accuracy of automatic interpretation using computer can not generally satisfy the various requirements of the projects. Now how quickly and accurately to extract Area Feature from remote sensing images is still a very important research direction.Now many experts and researchers have designed a lot of distinctive extraction algorithm in their field to extract area features and made many results. The main methods to extract linear features are: analysis based on Morphology, neural network analysis, least squares matching, dynamic programming algorithm, etc. The main methods to extract area features are: analysis based on Morphology, texture analysis methods, statistical analysis, region growing methods, etc. According to the research needs, this paper carried out the following several aspects of research and exploration:(1) First remote sensing image information extraction, object types and other related concepts were expounded. And then the overall process of extraction area feature from the remote sensing images was preliminary designed;(2) Preprocessing of remote sensing images was studied. A variety of preprocessing algorithms were achieved, and the pros and cons of various pretreatment methods were implemented.(3) A variety of methods based on texture analysis were researched and implemented and the results of using these methods to extract the area feature were analyzed.(4) Statistical distribution matching was conducted discussion and exploration in-depth. The main idea of this method is: according to the similarity of the spatial distribution of the same area features, area feature were extracted by being considered both the location and the intensity information. According to the different implementations, two specific types are: the statistical distribution movement matching and the same gray distance matching. According to the results of extracting information, this method was evaluated and the recommendations were made for further research.Through the research and exploration on the above aspects, the extraction methods used in this study were analyzed and summarized according to the results of extraction and the recommendations of the future development in this field were raised.