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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >A Stable Land Cover Patches Method for Automatic Registration of Multitemporal Remote Sensing Images
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A Stable Land Cover Patches Method for Automatic Registration of Multitemporal Remote Sensing Images

机译:一种用于多时相遥感影像自动配准的稳定地表覆盖方法

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We propose a stable land cover patches method (SLCPM) for the automatic registration of multitemporal remote sensing images. Our method takes advantage of multispectral features as well as stable and widespread land cover patches in remote sensing images. We tested our method using optical satellite images through four different experiments. In the first and second experiments, we tried to register images acquired on different days but by the same sensor. In the third experiment, we tried to register images acquired on different days and by different sensors (which means different spectral resolutions were also considered). In the fourth experiment, we tried to register images with different spatial resolutions acquired on different dates and by different sensors. Three indices were used in our paper for quality evaluation: overall Root Mean Square Error (RMS$_{rm all}$), Root Mean Square Error calculated by the leave-one-out method (RMS$_{rm LOO}$ ), and a statistical evaluation of the goodness of control point (CP) distribution across the image (S$_{rm cat}$). Results showed SLCPM could generate sufficient, accurate and well distributed CP pairs. We further compared our method with two other popular automatic image registration methods-scale invariant feature transform (SIFT) and a contour-based approach. The contour-based approach could hardly generate any CPs in all the experiments, while SIFT performed very well in the first three experiments both in accuracy and distribution of CPs but was ineffective in the most complex (i.e., last) experiment, due to the lack of correct CPs.
机译:我们提出了一种稳定的土地覆盖补丁方法(SLCPM),用于多时相遥感影像的自动配准。我们的方法利用了多光谱特征以及遥感图像中稳定且广泛的土地覆盖斑块。我们通过四个不同的实验使用光学卫星图像测试了我们的方法。在第一个和第二个实验中,我们尝试记录在同一天通过同一传感器获取的图像。在第三个实验中,我们尝试记录在不同日期和通过不同传感器获取的图像(这意味着还要考虑不同的光谱分辨率)。在第四个实验中,我们尝试配准在不同日期和通过不同传感器获取的具有不同空间分辨率的图像。我们的论文中使用了三个指标来进行质量评估:总体均方根误差(RMS $ _ {rm all} $ ) ,通过留一法计算的均方根误差(RMS $ _ {rm LOO} $ ),以及对整个图像上控制点(CP)分布的良好程度的统计评估(S $ _ {rm cat} $ ) 。结果表明,SLCPM可以生成足够,准确和分布良好的CP对。我们进一步将我们的方法与其他两种流行的自动图像配准方法(比例不变特征变换(SIFT))和基于轮廓的方法进行了比较。基于轮廓的方法几乎不可能在所有实验中生成任何CP,而SIFT在前三个实验中在CP的准确性和分布方面均表现出色,但由于缺乏,在最复杂(即最后一个)实验中效果不佳正确的CP。

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