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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Testing the Efficiency of Using High-Resolution Data From GF-1 in Land Cover Classifications
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Testing the Efficiency of Using High-Resolution Data From GF-1 in Land Cover Classifications

机译：测试在土地覆被分类中使用GF-1高分辨率数据的效率

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摘要

High-resolution remote sensing plays an important role in the study of subtle changes on the Earth's surface. The newly orbiting Chinese GF-1 satellites are designed to observe the Earth surface on a regional scale; however, the satellite efficiency requires further investigation. In this paper, the efficiency of using GF-1 01 satellite images to monitor a complex surface is tested by considering supplementary information and different land cover classification methods. Our work revealed that the GF-1 satellite observations can efficiently detect land cover fragments. When the support vector machine method is applied, the overall classification accuracy based on multisource data reaches 90.5%. The “salt and pepper phenomenon” is effectively reduced in classification images. These results also indicate that the accuracy of the GF-1 image classification is superior to the results when using the same method with the Landsat 8 and Sentinel-2A images, with the overall classification accuracy increasing by 23.6% and 13.6%. Our study suggests that GF-1 satellite observations are suitable for land cover studies on complex land surfaces. This approach can benefit various related fields such as land resource surveys, ecological assessments, environmental evaluations, and so forth.
机译：高分辨率遥感在研究地球表面的细微变化方面起着重要作用。新近运行的中国GF-1卫星旨在观察区域范围内的地球表面。但是，卫星效率需要进一步调查。本文通过考虑补充信息和不同的土地覆盖分类方法，测试了使用GF-1 01卫星图像监测复杂表面的效率。我们的工作表明，GF-1卫星观测可以有效地探测土地覆盖碎片。应用支持向量机方法时，基于多源数据的总体分类精度达到90.5％。在分类图像中有效减少了“盐和胡椒现象”。这些结果还表明，GF-1图像分类的准确性优于使用Landsat 8和Sentinel-2A图像的相同方法时的结果，总体分类准确性分别提高了23.6％和13.6％。我们的研究表明，GF-1卫星观测适用于复杂土地表面的土地覆盖研究。这种方法可以使各种相关领域受益，例如土地资源调查，生态评估，环境评估等。

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