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首页> 外文会议>SPIE Conference on Remote Sensing for Environmental Monitoring, GIS Applications, and Geology >Algorithm for image fusion based on DEM and remote sensing image
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Algorithm for image fusion based on DEM and remote sensing image

机译：基于DEM和遥感图像的图像融合算法

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Terrain roughness and vegetation growth are important influence factors of environment. But it is very difficult todescribe the terrain roughness in remote sensing image. Although with the launch of TERRA, Moderate resolutionImaging Spectroradiometer (MODIS), with abundant information, quickly acquiring data and wide range of coverage, isa new data for classication of land area. Considering terrain is still very important problem. This study considered thecharacteristics of Zhejiang land planting. The digital slope image derived from the DEM map and multitemporalMODIS were used for the purpose of improving the classification accuracy of MODIS in large hilly region. Twomethods have been employed till now. One is visual interpretation using digital images, and the other is the automatedextraction of landform characteristics from DEM. Thus,the results obtained from the first method are difficult for futureuse. As to the second method, it is difficult to get detailed classifications for example, to distinguish a valley plain froman open plain by using DEM alone due to the complex nature in landform characteristics. In fact, DEM and digitalimage contain different, yet complementary, information related to landform features. Therefore, a new method tointegrate landform information of both DEM and MODIS and NOAA-AVHRR image by digitizing signing lines inMODIS and NOAA-AVHRR image is presented in this paper. With this approach, different results of basic landformswere successfully classified and mapped automatically in Zhejiang Province. In addition, the spatial variability ofaccuracy in classification was also evaluated by sampling points based on an application of Globe positioning system(GPS).
机译：地形粗糙度和植被增长是环境的重要影响因素。但它非常困难地在遥感图像中汲取地形粗糙度。虽然通过推出Terra，适度分辨率光谱仪（MODIS），但信息丰富，迅速获取数据和广泛的覆盖范围，是土地面积分类的新数据。考虑到地形仍然是非常重要的问题。本研究认为浙江土地种植的组成。从DEM图和Multi8pormodis导出的数字斜率图像用于提高大丘陵区MODIS的分类准确性。直到现在雇用了Twomethods。一个是使用数字图像的视觉解释，另一个是来自DEM的地形特征的自动伸出。因此，从第一种方法获得的结果很难对ufufeuse。关于第二种方法，例如，难以获得详细的分类，以通过使用地形特性中的复杂性质来利用Dem独自使用DEM来区分谷普通普通普通普通普通普通普通的分类。事实上，DEM和DIGNITIMAGE包含与地形功能相关的不同但互补的信息。因此，本文介绍了通过数字化签名线和NOAA-AVHRR图像来扩展DEM和MODIS和NOAA-AVHRR图像的新方法。采用这种方法，基本地位的不同成果在浙江省成功分类和映射。此外，还通过基于全球定位系统（GPS）的应用，通过采样点评估分类中累计的空间变异性。

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