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An Object-Based Linear Weight Assignment Fusion Scheme to Improve Classification Accuracy Using Landsat and MODIS Data at the Decision Level
Guan, Xudong1,2; Liu, Gaohuan1; Huang, Chong1; Liu, Qingsheng1; Wu, Chunsheng1; Jin, Yan1,2; Li, Yafei3
2017-12-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume55Issue:12Pages:6989-7002
Corresponding AuthorHuang, Chong(huangch@lreis.ac.cn)
AbstractLandsat satellite images are extensively used in land-use studies due to their relatively high spatial resolution. However, the number of usable data sets is limited by the relatively long revisit interval and phenology effects can significantly reduce classification accuracy. Moderate Resolution Imaging Spectroradiometer (MODIS) images have higher temporal frequency and can provide extra time-series information. However, they are limited in their capability to classify heterogeneous landscapes due to their coarse spatial resolution. Fusion of different data sources is a potential solution for improving land-cover classification. This paper proposes a fusion scheme to combine Landsat and MODIS remote sensing data at the decision level. First, multiresolution segmentations on the two kinds of remote sensing data are performed to identify the landscape objects and are used as fusion units in subsequent steps. Then, fuzzy classifications are applied to each of the two different resolution data sets and the classification accuracies are evaluated. According to the performance of the two data sets in classification evaluation, a simple weight assignment technique based on the weighted sum of the membership of imaged objects is implemented in the final classification decision. The weighting factors are calculated based on a confusion matrix and the heterogeneity of detected land cover. The algorithm is capable of integrating the time-series spectral information of MODIS data with spatial contexts extracted from Landsat data, thus improving the land-cover classification accuracy. The overall classification accuracy using the fusion technique increased by 7.43% and 10.46% compared with the results from the individual Landsat and MODIS data, respectively.
KeywordDecision fusion fuzzy sets image classification image segmentation remote sensing time-series analysis
DOI10.1109/TGRS.2017.2737780
WOS KeywordYELLOW-RIVER DELTA ; NDVI TIME-SERIES ; SURFACE REFLECTANCE ; COMPLEX LANDSCAPES ; VEGETATION ; COVER ; CHINA ; INTEGRATION ; DYNAMICS ; SCIENCE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41661144030] ; National Natural Science Foundation of China[41471335] ; National Natural Science Foundation of China[41561144012] ; National Natural Science Foundation of China[41501430] ; LREIS[O88RA303YA] ; National Science-Technology Support Plan Projects[2013BAD05B03]
Funding OrganizationNational Natural Science Foundation of China ; LREIS ; National Science-Technology Support Plan Projects
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000417363100026
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/60809
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHuang, Chong
Affiliation1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Civil Aviat Univ China, Tianjin 300300, Peoples R China
Recommended Citation
GB/T 7714
Guan, Xudong,Liu, Gaohuan,Huang, Chong,et al. An Object-Based Linear Weight Assignment Fusion Scheme to Improve Classification Accuracy Using Landsat and MODIS Data at the Decision Level[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2017,55(12):6989-7002.
APA Guan, Xudong.,Liu, Gaohuan.,Huang, Chong.,Liu, Qingsheng.,Wu, Chunsheng.,...&Li, Yafei.(2017).An Object-Based Linear Weight Assignment Fusion Scheme to Improve Classification Accuracy Using Landsat and MODIS Data at the Decision Level.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,55(12),6989-7002.
MLA Guan, Xudong,et al."An Object-Based Linear Weight Assignment Fusion Scheme to Improve Classification Accuracy Using Landsat and MODIS Data at the Decision Level".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 55.12(2017):6989-7002.
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