State Bureau of Surveying and Mapping

About: State Bureau of Surveying and Mapping is a based out in . It is known for research contribution in the topics: Terrain & Pixel. The organization has 277 authors who have published 206 publications receiving 2044 citations.

Combining multiple algorithms for road network tracking from multiple source remotely sensed imagery: a practical system and performance evaluation.

Abstract: In light of the increasing availability of commercial high-resolution imaging sensors, automatic interpretation tools are needed to extract road features. Currently, many approaches for road extraction are available, but it is acknowledged that there is no single method that would be successful in extracting all types of roads from any remotely sensed imagery. In this paper, a novel classification of roads is proposed, based on both the roads' geometrical, radiometric properties and the characteristics of the sensors. Subsequently, a general road tracking framework is proposed, and one or more suitable road trackers are designed or combined for each type of roads. Extensive experiments are performed to extract roads from aerial/satellite imagery, and the results show that a combination strategy can automatically extract more than 60% of the total roads from very high resolution imagery such as QuickBird and DMC images, with a time-saving of approximately 20%, and acceptable spatial accuracy. It is proven that a combination of multiple algorithms is more reliable, more efficient and more robust for extracting road networks from multiple-source remotely sensed imagery than the individual algorithms.

Detection of urban sprawl using a genetic algorithm-evolved artificial neural network classification in remote sensing: a case study in Jiading and Putuo districts of Shanghai, China

Abstract: Urban sprawl results in the most complex process of land use and land cover change, which in turn has a compound impact on the structure and function of ecosystems in urban areas. The detection of urban sprawl based on remote sensing was studied in two districts of Shanghai, China. The study area includes Jiading district which is one of the fastest developing urban fringe areas, and Putuo district which is one of the downtown areas in Shanghai. The structure of the artificial backpropagation neural network (BPN) classifier was evolved by genetic algorithm (GA), including the connection values between neurons, hidden layer numbers and their neurons, and neuron correction values in all layers. A comparison of the proposed method was made with conventional classification methods such as the minimum distance (MD) classifier, maximum likelihood (ML) classifier and improved backpropagation neural network classifier. The result shows that the proposed approach has higher accuracy and reliability for the classification of remotely sensed data. Therefore, three epochs of Landsat Thematic Mapper (TM) imageries of the study area were selected in 1990, 2000 and 2006, and the changes of urban lands for different time intervals were detected. A comparison of the two districts and their towns was also made, which characterizes urban sprawl in the typical urban fringe and downtown areas of Shanghai.

Fuzzy topological relations between fuzzy spatial objects

Abstract: Fuzziness is an internal property of spatial objects. How to model fuzziness of a spatial object is a main task of next generation GIS. This paper proposes basic fuzzy spatial object types based on fuzzy topology. These object types are the natural extension of current non-fuzzy spatial object types. A fuzzy cell complex structure is defined for modeling fuzzy regions, lines and points. Furthermore, fuzzy topological relations between these fuzzy spatial objects are formalized based on the 9-intersection approach. This model can be implemented for GIS applications due to its scientific theory basis.

Reasoning about changes of land covers with fuzzy settings

Abstract: Change detection of land cover from satellite images is emphasized in China as a result of rapid land cover changes. This paper discusses a method to calculate land cover changes based on fuzzy settings. In this method, land cover is regarded as fuzzy spatial objects rather than crisp objects. The fuzzy land cover is derived based on a fuzzy classification. The degree of change is then calculated using fuzzy reasoning. In order to minimize any errors in image matching, fuzzy polygons are adopted in the reasoning. By incorporating spectral value differences, the errors in image classification can be reduced considerably. The land cover changes from 1989 to 1998 in a part of Sanya City of China are inferred as an example. The results show that this fuzzy method is able to identify the land cover changes more precisely than the crisp method. Furthermore, the transitional land cover changes can be revealed as a by‐product.