Comparison of Layer-stacking and Dempster-Shafer Theory-based Methods Using Sentinel-1 and Sentinel-2 Data Fusion in Urban Land Cover Mapping

被引:16
作者
Bui, Dang Hung [1 ,2 ]
Mucsi, Laszlo [1 ]
机构
[1] Univ Szeged, Dept Geoinformat Phys & Environm Geog, Szeged, Hungary
[2] Ind Univ Ho Chi Minh City, Inst Environm Sci Engn & Management, Ho Chi Minh City, Vietnam
基金
匈牙利科学研究基金会;
关键词
Land cover mapping; data fusion; random forest; Dempster-Shafer theory; optical data; radar data; pixel level; decision level; USE CLASSIFICATION; SAR; MULTISENSOR; INTEGRATION; DYNAMICS; IMAGES;
D O I
10.1080/10095020.2022.2035656
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Data fusion has shown potential to improve the accuracy of land cover mapping, and selection of the optimal fusion technique remains a challenge. This study investigated the performance of fusing Sentinel-1 (S-1) and Sentinel-2 (S-2) data, using layer-stacking method at the pixel level and Dempster-Shafer (D-S) theory-based approach at the decision level, for mapping six land cover classes in Thu Dau Mot City, Vietnam. At the pixel level, S-1 and S-2 bands and their extracted textures and indices were stacked into the different single-sensor and multi-sensor datasets (i.e. fused datasets). The datasets were categorized into two groups. One group included the datasets containing only spectral and backscattering bands, and the other group included the datasets consisting of these bands and their extracted features. The random forest (RF) classifier was then applied to the datasets within each group. At the decision level, the RF classification outputs of the single-sensor datasets within each group were fused together based on D-S theory. Finally, the accuracy of the mapping results at both levels within each group was compared. The results showed that fusion at the decision level provided the best mapping accuracy compared to the results from other products within each group. The highest overall accuracy (OA) and Kappa coefficient of the map using D-S theory were 92.67% and 0.91, respectively. The decision-level fusion helped increase the OA of the map by 0.75% to 2.07% compared to that of corresponding S-2 products in the groups. Meanwhile, the data fusion at the pixel level delivered the mapping results, which yielded an OA of 4.88% to 6.58% lower than that of corresponding S-2 products in the groups.
引用
收藏
页码:425 / 438
页数:14
相关论文
共 48 条
[1]   Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data [J].
Abdi, Abdulhakim Mohamed .
GISCIENCE & REMOTE SENSING, 2020, 57 (01) :1-20
[2]   Improving accuracy evaluation of Landsat-8 OLI using image composite and multisource data with Google Earth Engine [J].
Adepoju, Kayode A. ;
Adelabu, Samuel A. .
REMOTE SENSING LETTERS, 2020, 11 (02) :107-116
[3]  
[Anonymous], 2019, Statistical yearbook of Binh Duong 2018
[4]   Assessing changes in the value of ecosystem services in response to land-use/land-cover dynamics in Nigeria [J].
Arowolo, Aisha Olushola ;
Deng, Xiangzheng ;
Olatunji, Olusanya Abiodun ;
Obayelu, Abiodun Elijah .
SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 636 :597-609
[5]   Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach [J].
Ban, Yifang ;
Hu, Hongtao ;
Rangel, I. M. .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2010, 31 (06) :1391-1410
[6]   Random forest in remote sensing: A review of applications and future directions [J].
Belgiu, Mariana ;
Dragut, Lucian .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2016, 114 :24-31
[7]  
Boivin C., 2020, DST USING THEORY BEL
[8]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[9]   Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme [J].
Cai, Guoyin ;
Ren, Huiqun ;
Yang, Liuzhong ;
Zhang, Ning ;
Du, Mingyi ;
Wu, Changshan .
SENSORS, 2019, 19 (14)
[10]   Data classification using the Dempster-Shafer method [J].
Chen, Qi ;
Whitbrook, Amanda ;
Aickelin, Uwe ;
Roadknight, Chris .
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2014, 26 (04) :493-517