热红外与可见光数据的云边缘高度匹配
Cloud edge heights matching between thermal infrared and visible data
- 2016年20卷第6期 页码:1435-1445
纸质出版日期: 2016
DOI: 10.11834/jrs.20165234
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纸质出版日期: 2016 ,
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[1]李彬,辛晓洲,张海龙,胡继超.热红外与可见光数据的云边缘高度匹配[J].遥感学报,2016,20(06):1435-1445.
LI Bin, XIN Xiaozhou, ZHANG Hailong, et al. Cloud edge heights matching between thermal infrared and visible data[J]. Journal of Remote Sensing, 2016,20(6):1435-1445.
高分辨率数据中云高度的差异性突显
特别是边缘处高度在云阴影识别和地表辐射估算等方面成为需要考虑的重要因素。热红外数据获取云高度分辨率较低、缺乏细部差异性特征
为解决这一问题
首先将对应的热红外和可见光数据进行特征点配准
再将基于热红外数据计算的云高度重采样至高分辨率
然后以基于欧式距离变换的围线搜索方法及距离加权将热红外云边缘高度匹配至对应的可见光图像
最后根据云阴影的相似度匹配方法确定真实云高度。结果表明
算法在遵循热红外云高信息分布变化规律的同时
可以得到较准确的高分辨率云边缘高度
一定程度上解决了热红外技术获取云高在分辨率上的局限
扩展了其在云高反演方面的作用。
Differences in cloud height are obvious in high-resolution data
especially because cloud edge heights have become an important factor in cloud shadow identification and estimation of surface solar radiation.However
the resolution of cloud heights calculated by thermal infrared data is low and lacks detailed characteristics.Cloud edges of visible and thermal infrared bands differ considerably both in shape and geometric features.The edge of high-resolution image has rich characteristics
whereas that of thermal infrared cloud height data is single and fuzzy in geometric characteristics
so they cannot match exactly.Although some feature points of clouds can be obtained by some feature point matching methods such as Scale-Invariant Feature Transform(SIFT) and Harris
the difference between two data on geometric features made available by feature points was less.This result cannot satisfy the need to match the information of thermal infrared cloud heights and high-resolution cloud edge data
and obtain a result with abundant diversity.To solve this problem
an algorithm was presented in this study.First
SIFT algorithm was utilized in this method to extract feature points for further image registration and correction.Then
cloud edge heights were calculated by thermal infrared data and re-sampled to a high resolution.Next
Euclidean distance transform was performed for each cloud edge pixel of high-resolution data to all thermal infrared cloud edge pixels
which could obtain spatial relationships between the two types of data.As the two types of data differed considerably in edge characteristics
directly determining the optimal matching point was difficult.Thus
a hierarchical searching method was used here.While the searched objects had different significance to matching points
the weight was given by distance to determine the final matching height.Finally
real cloud heights were determined according to the matching method of cloud shadow similarity.We used five images of HJ-1B CCD and IRS data in the Heihe area on June 8
2012.From all the matched results
the corresponding regions of the matched results had high cloud heights where thermal infrared data also had high cloud heights.At the same time
resolution and details were improved.To evaluate the accuracy of the calculated heights
we selected 10 highly recognizable feature shadow points in each image and marked their coordinates in the image.We set the actual shadow points as reference and calculated the offset of the same feature shadow point
in which we could obtain the error of each cloud height.We found that the errors between 0.1 km to 0.3 km were 70%among all 50 points
12%were less than 0.1 km
and 0.25 km was the average error of all points.Compared with other studies
cloud height accuracy was higher in our study.Also
we chose SIFT algorithm to match cloud heights by using two types of data and compared the matched results of some feature points with our method.The height accuracy obtained by SIFT was lower than that of our algorithm.In addition
unlike some feature point matching methods
our method can complete full-information matching.A cloud edge height matching method based on Euclidean distance transform by hierarchical searching is proposed in our paper.The method can match the cloud height information of low-resolution thermal infrared to corresponding cloud edge of high-resolution image.Experimental results showed that the matched results followed the distribution and variation law of thermal infrared cloud heights
as well as the cloud edge heights with high accuracy and detailed characteristics.To a certain extent
our study solved the resolution problem in obtaining cloud height by thermal infrared data.In addition
compared with some matching methods of feature points
our method could complete full-information matching and had a higher accuracy.However
the accuracy of matched results would be influenced by many factors
such as accuracy of cloud detection
surface in homogeneity
and image registration.The method in our paper is only for cloud edge height.Thus
matching for other parts of cloud still needs further research.
云边缘高度欧氏距离变换围线搜索云阴影
cloud edge heightsEuclidean distance transform(EDT)contour scanningcloud shadows
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