• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一种针对珠海一号卫星高光谱影像的全谱段配准方法。

A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery.

作者信息

Meng Jinjun, Wu Jiaqi, Lu Linlin, Li Qingting, Zhang Qiang, Feng Suyun, Yan Jun

机构信息

Zhuhai Orbita Aerospace Science & Technology Co., Ltd., Zhuhai 519080, China.

Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

出版信息

Sensors (Basel). 2020 Nov 5;20(21):6298. doi: 10.3390/s20216298.

DOI:10.3390/s20216298
PMID:33167410
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7663805/
Abstract

Accurate registration is an essential prerequisite for analysis and applications involving remote sensing imagery. It is usually difficult to extract enough matching points for inter-band registration in hyperspectral imagery due to the different spectral responses for land features in different image bands. This is especially true for non-adjacent bands. The inconsistency in geometric distortion caused by topographic relief also makes it inappropriate to use a single affine transformation relationship for the geometric transformation of the entire image. Currently, accurate registration between spectral bands of Zhuhai-1 satellite hyperspectral imagery remains challenging. In this paper, a full-spectrum registration method was proposed to address this problem. The method combines the transfer strategy based on the affine transformation relationship between adjacent spectrums with the differential correction from dense Delaunay triangulation. Firstly, the scale-invariant feature transform (SIFT) extraction method was used to extract and match feature points of adjacent bands. The RANdom SAmple Consensus (RANSAC) algorithm and the least square method is then used to eliminate mismatching point pairs to obtain fine matching point pairs. Secondly, a dense Delaunay triangulation was constructed based on fine matching point pairs. The affine transformation relation for non-adjacent bands was established for each triangle using the affine transformation relation transfer strategy. Finally, the affine transformation relation was used to perform differential correction for each triangle. Three Zhuhai-1 satellite hyperspectral images covering different terrains were used as experiment data. The evaluation results showed that the adjacent band registration accuracy ranged from 0.2 to 0.6 pixels. The structural similarity measure and cosine similarity measure between non-adjacent bands were both greater than 0.80. Moreover, the full-spectrum registration accuracy was less than 1 pixel. These registration results can meet the needs of Zhuhai-1 hyperspectral imagery applications in various fields.

摘要

精确配准是涉及遥感影像分析和应用的基本前提。由于不同图像波段中地物的光谱响应不同，在高光谱影像中通常难以提取足够的匹配点用于波段间配准。对于非相邻波段尤其如此。地形起伏引起的几何畸变不一致也使得对整个图像进行几何变换时使用单一仿射变换关系不合适。目前，珠海一号卫星高光谱影像波段间的精确配准仍然具有挑战性。本文提出了一种全光谱配准方法来解决这一问题。该方法将基于相邻光谱间仿射变换关系的传递策略与密集德劳内三角剖分的微分校正相结合。首先，使用尺度不变特征变换（SIFT）提取方法提取并匹配相邻波段的特征点。然后使用随机抽样一致性（RANSAC）算法和最小二乘法消除不匹配点对，以获得精确的匹配点对。其次，基于精确匹配点对构建密集德劳内三角剖分。利用仿射变换关系传递策略为每个三角形建立非相邻波段的仿射变换关系。最后，使用仿射变换关系对每个三角形进行微分校正。使用覆盖不同地形的三幅珠海一号卫星高光谱图像作为实验数据。评估结果表明，相邻波段配准精度在0.2到0.6像素之间。非相邻波段之间的结构相似性度量和余弦相似性度量均大于0.80。此外，全光谱配准精度小于1像素。这些配准结果能够满足珠海一号高光谱影像在各个领域的应用需求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/41c6de89ab0c/sensors-20-06298-g013a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/ebf8af531f84/sensors-20-06298-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/e40a4a219fd1/sensors-20-06298-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/e35ea3d46be5/sensors-20-06298-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/df8a1f2d67d6/sensors-20-06298-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/2de8db53c960/sensors-20-06298-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/4aa9e526dd19/sensors-20-06298-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/2e2c2d80a65b/sensors-20-06298-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/d9c39b456928/sensors-20-06298-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/3558b5476364/sensors-20-06298-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/080f33782a57/sensors-20-06298-g010a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/51664e1890b2/sensors-20-06298-g011a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/14efdf433296/sensors-20-06298-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/41c6de89ab0c/sensors-20-06298-g013a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/ebf8af531f84/sensors-20-06298-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/e40a4a219fd1/sensors-20-06298-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/e35ea3d46be5/sensors-20-06298-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/df8a1f2d67d6/sensors-20-06298-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/2de8db53c960/sensors-20-06298-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/4aa9e526dd19/sensors-20-06298-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/2e2c2d80a65b/sensors-20-06298-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/d9c39b456928/sensors-20-06298-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/3558b5476364/sensors-20-06298-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/080f33782a57/sensors-20-06298-g010a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/51664e1890b2/sensors-20-06298-g011a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/14efdf433296/sensors-20-06298-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1dc/7663805/41c6de89ab0c/sensors-20-06298-g013a.jpg

相似文献

1
A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery.一种针对珠海一号卫星高光谱影像的全谱段配准方法。
Sensors (Basel). 2020 Nov 5;20(21):6298. doi: 10.3390/s20216298.
2
Satellite-Borne Optical Remote Sensing Image Registration Based on Point Features.基于点特征的星载光学遥感图像配准
Sensors (Basel). 2021 Apr 11;21(8):2695. doi: 10.3390/s21082695.
3
A Coarse-to-Fine Geometric Scale-Invariant Feature Transform for Large Size High Resolution Satellite Image Registration.一种用于大尺寸高分辨率卫星图像配准的从粗到精的几何尺度不变特征变换
Sensors (Basel). 2018 Apr 27;18(5):1360. doi: 10.3390/s18051360.
4
Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm.利用光流算法开发用于河流高光谱图像的图像配准技术。
Sensors (Basel). 2021 Mar 31;21(7):2407. doi: 10.3390/s21072407.
5
Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images.高光谱图像的谱-空尺度不变特征变换。
IEEE Trans Image Process. 2018 Feb;27(2):837-850. doi: 10.1109/TIP.2017.2749145. Epub 2017 Sep 4.
6
[The meteorological satellite spectral image registration based on Fourier-Mellin transform].基于傅里叶-梅林变换的气象卫星光谱图像配准
Guang Pu Xue Yu Guang Pu Fen Xi. 2013 Mar;33(3):855-8.
7
[Hyperspectral Band Selection Based on Spectral Clustering and Inter-Class Separability Factor].基于光谱聚类和类间可分离性因子的高光谱波段选择
Guang Pu Xue Yu Guang Pu Fen Xi. 2015 May;35(5):1357-64.
8
Image registration method for multimodal images.多模态图像的图像配准方法。
Appl Opt. 2011 May 1;50(13):1861-7. doi: 10.1364/AO.50.001861.
9
[Research on non-rigid medical image registration algorithm based on SIFT feature extraction].基于尺度不变特征变换（SIFT）特征提取的非刚性医学图像配准算法研究
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2010 Aug;27(4):763-8, 784.
10
An Improved ASIFT Image Feature Matching Algorithm Based on POS Information.一种基于POS信息的改进型ASIFT图像特征匹配算法
Sensors (Basel). 2022 Oct 12;22(20):7749. doi: 10.3390/s22207749.

本文引用的文献

1
Urban sprawl in provincial capital cities in China: evidence from multi-temporal urban land products using Landsat data.中国省会城市的城市扩张：基于Landsat数据的多时相城市土地产品的证据
Sci Bull (Beijing). 2019 Jul 30;64(14):955-957. doi: 10.1016/j.scib.2019.04.036. Epub 2019 May 3.
2
An Image Registration Method for Multisource High-Resolution Remote Sensing Images for Earthquake Disaster Assessment.一种用于地震灾害评估的多源高分遥感图像的图像配准方法。
Sensors (Basel). 2020 Apr 17;20(8):2286. doi: 10.3390/s20082286.
3
Assessment of urban environmental change using multi-source remote sensing time series (2000-2016): A comparative analysis in selected megacities in Eurasia.
利用多源遥感时间序列评估城市环境变化（2000 - 2016年）：欧亚大陆部分特大城市的比较分析
Sci Total Environ. 2019 Sep 20;684:567-577. doi: 10.1016/j.scitotenv.2019.05.344. Epub 2019 May 25.
4
A Continuation Method for Graph Matching Based Feature Correspondence.一种基于图匹配的特征对应延续方法。
IEEE Trans Pattern Anal Mach Intell. 2020 Aug;42(8):1809-1822. doi: 10.1109/TPAMI.2019.2903483. Epub 2019 Mar 6.
5
Automated Geo/Co-Registration of Multi-Temporal Very-High-Resolution Imagery.多时相超高分辨率影像的自动地理/配准。
Sensors (Basel). 2018 May 17;18(5):1599. doi: 10.3390/s18051599.
6
Mineral resources prospecting by synthetic application of TM/ETM+, Quickbird and Hyperion data in the Hatu area, West Junggar, Xinjiang, China.中国新疆西准噶尔哈图地区TM/ETM+、快鸟和Hyperion数据综合应用的矿产资源勘查
Sci Rep. 2016 Feb 25;6:21851. doi: 10.1038/srep21851.
7
Optimal randomized RANSAC.最优随机抽样一致性算法
IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1472-82. doi: 10.1109/TPAMI.2007.70787.
8
Geometric distortion correction for echo planar images using nonrigid registration with spatially varying scale.使用具有空间变化尺度的非刚性配准对回波平面图像进行几何失真校正。
Magn Reson Imaging. 2008 Dec;26(10):1388-97. doi: 10.1016/j.mri.2008.03.004. Epub 2008 May 21.
9
An FFT-based technique for translation, rotation, and scale-invariant image registration.基于快速傅里叶变换的平移、旋转和尺度不变图像配准技术。
IEEE Trans Image Process. 1996;5(8):1266-71. doi: 10.1109/83.506761.
10
Extension of phase correlation to subpixel registration.将相位相关扩展到亚像素配准。
IEEE Trans Image Process. 2002;11(3):188-200. doi: 10.1109/83.988953.