| 基于证据理论的多源遥感产品土地覆被分类精度优化 |
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| 引用本文： | 宋宏利,张晓楠,陈宜金.基于证据理论的多源遥感产品土地覆被分类精度优化[J].农业工程学报,2014,30(14):132-139. |
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| 作者姓名： | 宋宏利 张晓楠 陈宜金 |
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| 作者单位： | 1. 河北工程大学资源学院,邯郸 056038; 2. 中国矿业大学《北京》地球科学与测绘工程学院,北京 100083;;1. 河北工程大学资源学院,邯郸 056038;;2. 中国矿业大学《北京》地球科学与测绘工程学院,北京 100083; |
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| 基金项目： | 河北省自然科学基金"大尺度多源遥感信息融合土地覆被制图研究"（D2013402014）;矿山空间信息技术国家测绘地理信息局重点实验室基金"大尺度多源土地覆被遥感产品融合研究"（KLM201206）;邯郸市科学发展计划项目（1321103076-4） |
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| 摘 要： | 针对现有土地覆被遥感产品及融合方法存在的不足,该文提出了一种新的分类体系转换方法,实现了证据理论(Dempster-Shafer)框架下多源产品的集成,并以GEOWIKI、林业调查数据为参考,通过绝对及交叉验证方法对融合结果精度进行了评价。研究结果表明:无论总体精度还是类别精度,融合结果与原始数据相比均有一定提高,说明在融合过程中,吸收了多源数据的类别分布特征,做到了多源数据间的互补。通过融合结果的不确定性分析,总体上融合结果的不确定性较小,但在景观异质性较强区域,融合结果的不确定性显著,不确定性值集中于0.4~0.7之间,这说明如何提高景观异质性区域的土地覆被类别精度,实现该区域数据重构是未来亟需解决的问题。该文所得成果为未来全球或区域尺度土地覆被遥感产品的研制及产品精度验证提供了参考。
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| 关 键 词： | 遥感 不确定性分析 分类 证据理论 土地覆被 遥感产品 数据融合 |
| 收稿时间： | 2014/3/25 0:00:00 |
| 修稿时间： | 7/3/2014 12:00:00 AM |
Land cover mapping using multi-sources data based on Dempster-Shafer theory |
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| Song Hongli,Zhang Xiaonan and Chen Yijin.Land cover mapping using multi-sources data based on Dempster-Shafer theory[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(14):132-139. |
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| Authors: | Song Hongli Zhang Xiaonan and Chen Yijin |
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| Abstract: | Abstract: The information on land cover at national scales is critical for addressing a range of problems, including the climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In view of the problems of the existed global land cover products and the deficiency of current data fusion methods, this study aims to develop a general framework for building a hybrid land cover map by the synergistic combination of a number of land-cover classifications with different legends and spatial resolutions based on Dempster Shafer theory. With the validation of GLOBCOVER, MODIS, GLC2000 and GLCNMO in regional and category scale, the results showed that MODIS had the best consistency with the referenced data, followed by GLC2000, and the GLCNMO and GLOBCOVER had the lower consistency with the referenced data. The validated products and reference data had some categorical confusions which mainly occurred in forest, grass, shrub and cropland, especially between shrub and other categories. So shrub had the worst classification precision. Confusions demonstrated the conspicuous regional characteristics, for example, in northeast, Tibet alpine zone and the southeast zone, the confusions mainly occurred in cropland and forest, grass and shrub, cropland and grass respectively. Based those experiences, the author computed the different category weight for four land cover products using the analytic hierarchy process, which will quantify the contribution in the merging process, and completed the land cover category transformation between four land cover products through the LCCS land cover system with eight indexes of the vegetation or no-vegetation, terrestrial or water, cultivated or natural, life type, leaf type and phenomena. A multi-source integrated land cover map was generated based on the Dempster-Shafer evidence theory. Based on the volunteered data from GEOWIKI project which was a validated program for global land cover products, the forest inventory data and cross-validation method, the author evaluated the fusion result, which showed that not only in overall accuracy but also in classification accuracy, the fusion map had an apparent improvement than original land cover products. For the GEOWIKI validation, the fusion map has the highest producer accuracy in forest, grassland, cropland and bare land, but the shrub classification accuracy is lower than that with GLCNMO; The permanent ice classification accuracy is lower than that with MODIS, the water classification accuracy is lower than that with MODIS and GLC2000. For the user accuracy, all the fusion has the highest accuracy except the water and permanent ice. This is because that in the validated category point, the shrub accounts for only 3.29%, the water accounts for only 0.78%, and the permanent ice accounts for 0.87%. For the forest data, the deviations of forest area percentage in all validated regions are all less than 5%. This demonstrates the fusion map is better for the category area scale, which explains that the evidence theory should absorb every land cover product's advantage in the fusion process and make the data complementary. The uncertainty analysis about fusion results show that the overall uncertainty is in a low range about 0-0.3, and this area covers about 94.5 percent of the total study area. For example, the areas with uncertainty value in 0-0.1 mainly is located in northwest, northeast, north china, Sichuan basin and south in Taiwan province. The area with uncertainty value greater than 0.3 is scarce but aggregated, such as the areas with uncertainty value in 0.3-0.4, mainly located in north center of Inner Mongolia, north of Xinjiang, the land cover in this region are grass land and bare land. The most uncertain area is located in northeast of Gansu province, Ningxia Hui Autonomous Region and northeast of Shaanxi province, and these regions' land covers are mainly grassland, cropland and bare land, which illustrate a conspicuous landscape heterogeneity characteristic. |
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| Keywords: | remote sensing uncertainty analysis classification Dempster-Shafer theory land cover remote sensing products data synthesis |
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