面向大区域遥感专题制图的自动化策略

如何消除相邻影像间的信息冗余是实现大区域遥感自动化制图的关键。本文提出了一种基于约束接缝线的区域分块自动化制图方法,首先采用全球通用墨卡托投影-UTM投影的网格带将研究区分为若干小区域;然后,针对每个UTM投影带内的遥感影像,采用影像镶嵌的原理和湖泊边界约束条件计算其接缝线网络,以确定每景遥感数据制图范围;最后,根据其制图范围生成对应的无冗余专题信息,减少计算量和相邻影像重复信息的编辑时间。本文以亚洲中部干旱区为例开展湖泊制图试验,结果证明该方法能够生成顾及湖泊的有效制图范围,提高了遥感大区域制图的效率。

An Automatic Scheme for the Remote Sensing Thematic Mapping in Large Area

Remotely sensed thematic mapping in large area is a hot and difficult topic in recent remote sensing mapping research. It is also a key factor that restricts the development of remote sensing application. The continental or global thematic mapping usually uses hundreds or thousands of scene images to cover the whole region, which causes the occurrence of overlaps in the coverage and induces time inconsistencies. How to eliminate the information redundancy between the adjacent images is becoming a crucial issue to the regional automatic thematic mapping in large area. The mapping accuracy and efficiency are the major sticking points in the large area mapping applications. In this paper, a new scheme designed for block splitting thematic mapping based on the constrained seamline networks is proposed. Firstly, the large study area is split into several small regions based on the uniform grids of Universal Transverse Mercator (UTM) Projection. Secondly, taking each uniform grid as a unit, with the lake boundary being set as the constraint, the mosaic seamlines are generated with respect to the image mosaic principles. Thus, each Landsat image is represented by a separated seamline polygon, which insures the lake boundaries would not be split. Thirdly, we combine the seamline networks of all UTM grids based on the generated seamlines between two adjacent UTM grids, and resultantly the seamline network for the whole region is built. Finally, each seamline polygon in the seamline network is taken as the valid mapping area of Landsat image, and then the lakes within each Landsat data are preserved inside the valid mapping area. The final lake mapping result is generated by combing all the lake layers. This method is tested on the Landsat 8 images for Central Asia in 2013 to generate a lake area map for the Central Asia region. 479 Landsat images are used to cover the whole study area. Except for the Aral Sea, Ala Nur, Balkash Lake and IssyKul Lake, all the other lakes lie inside the corresponding valid mapping areas. It is proved that the proposed method can effectively split the redundant area between two Landsat images; meanwhile, the lakes are not split by seamlines, so as to keep the integrity and accuracy of lakes. Compared with the lake mapping results in 2010, the number of lakes increased in 2013, while the areas of lakes decreased. The main reason of this phenomenon is that lakes in the plain deserts, such as Aral Sea, are experiencing changes of shrinking, while in the alpine regions, a lot of newly generated small lakes are emerging. The proposed method has two advantages: (1) during the image preprocessing stage, each Landsat image is analyzed to get a valid mapping area, and the lake mapping step is performed within this valid mapping area. As a result, each Landsat mapping region is restricted, which involves less computational time and editing work; (2) the valid mapping region is determined based on the lake constraint conditions; therefore, the seamline cannot cross the lake boundaries, which keeps the lake mapping results to be unique and accurate. However, this paper mainly focuses on the scheme and strategy of automatic thematic mapping, within which the detailed technologies of data flows and the seamline algorithm is still simplistic, and the accuracies and efficiencies of lake mapping is not thoroughly described. In future, its technical details and the stability of the algorithm will be improved continuously, and the continental or global scale lake mapping applications will be further studied.