IEEE Geoscience and Remote Sensing Magazine - December 2021 - 136

flow techniques were included. The performance of registration
software packages and tools was analyzed. Additionally,
novel registration evaluations were presented to support an
effective assessment. The development of any approach aims
to improve registration accuracy as much as possible because
registration is an important step for preprocessing remote
sensing images. Several such techniques have been developed,
as recounted in this article.
However, as resolutions increase, the problem of inconsistent
local distortion caused by high-rise buildings and
topographic relief has become apparent; this cannot be exactly
described by the transformation model. Moreover, WFV
images are an emerging trend in satellite image production,
enabling a whole ROI to be contained within one image.
This poses a challenge for real-time registration and memory
for registration processing. Therefore, we believe that future
research on remote sensing image registration will use accelerated
registration, combined approaches for remote sensing
image registration, heterogeneous and cross-scale image
registration, and smart registration. Challenges remain, and
considerable additional research is required. We perform this
research with the advantage of lower entrance barriers than
the TDOM generation.
ACKNOWLEDGMENTS
The work was supported by the National Natural Science
Foundation of China (grants 41971303 and 41701394), Key
Research and Development Program of Shaanxi Province
(grant 2020NY-166), and Fundamental Research Funds for
the Central Universities (grant GK202103143). The authors
thank the editor-in-chief and associate editor of IEEE Geoscience
and Remote Sensing Magazine as well as four anonymous
reviewers for their advice for strengthening their manuscript.
AUTHOR INFORMATION
Ruitao Feng (feng-rt@snnu.edu.cn) is with the School of
Geography and Tourism, Shaanxi Normal University, Xi'an,
710062, China.
Huanfeng Shen (shenhf@whu.edu.cn) is with the School
of Resource and Environment Science and the Collaborative
Innovation Center for Geospatial Technology, Wuhan University,
Wuhan, 430072, China. He is a Senior Member of IEEE.
Jianjun Bai (bjj@snnu.edu.cn) is with the School of Geography
and Tourism, Shaanxi Normal University, Xi'an,
710062, China.
Xinghua Li (lixinghua5540@whu.edu.cn) is with the
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan, 430072, China. He is a Senior Member
of IEEE.
REFERENCES
[1] A. Wong and D. A. Clausi, " ARRSI: Automatic registration of
remote-sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 45,
no. 5, pp. 1483-1493, 2007. doi: 10.1109/TGRS.2007.892601.
[2] X. Li, N. Hui, H. Shen, Y. Fu, and L. Zhang, " A robust mosaicking
procedure for high spatial resolution remote sensing images, "
136
ISPRS J. Photogram. Remote Sens., vol. 109, pp. 108-125, Nov.
2015. doi: 10.1016/j.isprsjprs.2015.09.009.
[3] H. Shen, X. Meng, and L. Zhang, " An Integrated Framework
for the Spatio-Temporal-Spectral Fusion of Remote Sensing
Images, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 12, pp.
7135-7148, 2016. doi: 10.1109/TGRS.2016.2596290.
[4] Y. Lu, P. Wu, X. Ma, and X. Li, " Detection and prediction of land
use/land cover change using spatiotemporal data fusion and the
Cellular Automata-Markov model, " Environ. Monitoring Assessment,
vol. 191, no. 2, p. 68, 2019. doi: 10.1007/s10661-019-7200-2.
[5] Z. Lv, T. Liu, C. Shi, J. A. Benediktsson, and H. Du, " Novel land
cover change detection method based on k-means clustering
and adaptive majority voting using bitemporal remote sensing
images, " IEEE Access, vol. 7, pp. 34,425-34,437, Jan. 2019. doi:
10.1109/ACCESS.2019.2892648.
[6] C. Yuan, F. Wang, S. Wang, and Y. Zhou, " Accuracy evaluation
of flood monitoring based on multiscale remote sensing
for different landscapes, " Geomatics, Natural Hazards Risk, vol.
10, no. 1, pp. 1389-1411, 2019. doi: 10.1080/19475705.2019.
1580224.
[7] L. Yang and G. Cervone, " Analysis of remote sensing imagery
for disaster assessment using deep learning: A case study
of flooding event, " Soft Comput., vol. 23, no. 24, pp. 13,393-
13,408, 2019. doi: 10.1007/s00500-019-03878-8.
[8] K. Barbieux, " Pushbroom hyperspectral data orientation by
combining feature-based and area-based co-registration techniques, "
Remote Sens., vol. 10, no. 4, p. 645, 2018. doi: 10.3390/
rs10040645.
[9] Y. Jiang, J. Wang, L. Zhang, G. Zhang, X. Li, and J. Wu, " Geometric
processing and accuracy verification of zhuhai-1 hyperspectral
satellites, " Remote Sens., vol. 11, no. 9, p. 996, 2019. doi:
10.3390/rs11090996.
[10] I. Aicardi, F. Nex, M. Gerke, and A. M. Lingua, " An image-based approach
for the co-registration of multi-temporal UAV image datasets, "
Remote Sens., vol. 8, no. 9, p. 779, 2016. doi: 10.3390/rs8090779.
[11] F. P. M. Oliveira and J. M. R. S. Tavares, " Medical image registration:
A review, " Comput. Methods Biomech. Biomed. Eng., vol. 17,
no. 2, pp. 73-93, 2014. doi: 10.1080/10255842.2012.670855.
[12] A. Sotiras, C. Davatzikos, and N. Paragios, " Deformable medical
image registration: A survey, " IEEE Trans. Med. Imag., vol. 32,
no. 7, pp. 1153-1190, 2013. doi: 10.1109/TMI.2013.2265603.
[13] M. A. Viergever, J. B. A. Maintz, S. Klein, K. Murphy, M. Staring,
and J. P. W. Pluim, " A survey of medical image registration, "
Med. Image Anal., vol. 33, pp. 140-144, Oct. 2016. doi:
10.1016/j.media.2016.06.030.
[14] G. Haskins, U. Kruger, and P. Yan, " Deep learning in medical
image registration: A survey, " Mach. Vis. Appl., vol. 31, nos. 1-2,
p. 8, 2020. doi: 10.1007/s00138-020-01060-x.
[15] L. G. Brown, " A survey of image registration techniques, "
ACM Comput. Surv., vol. 24, no. 4, pp. 325-376, 1992. doi:
10.1145/146370.146374.
[16] B. Zitová and J. Flusser, " Image registration methods: A survey, "
Image Vis. Comput., vol. 21, no. 11, pp. 977-1000, 2003. doi:
10.1016/S0262-8856(03)00137-9.
[17] M. Deshmukh and U. Bhosle, " A survey of image registration, "
Int. J. Image Process., vol. 5, no. 3, p. 245, 2011.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE DECEMBER 2021
IEEE Geoscience and Remote Sensing Magazine - December 2021

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - December 2021
