Landsat-8 Sensor and Sentinel-2 Sensor Data Fusion With Multiscale Detailed Information

被引:0
作者
Wang, Peng [1 ,2 ,3 ,4 ]
Du, Jun [4 ]
Wen, Xiongfei [2 ,5 ]
Hu, Caiping [1 ,3 ,6 ]
Ge, Lin [3 ]
Huang, Mingxuan [4 ]
机构
[1] China Univ Min & Technol Beijing, Natl Engn Res Ctr Coal Mine Water Hazard Controlli, Beijing, Peoples R China
[2] Changjiang River Sci Res Inst, Hubei Prov Key Lab Basin Water Resource & Ecoenvir, Wuhan 430010, Peoples R China
[3] Shandong Prov Geomineral Engn Explorat Inst, Inst Hydrogeol & Engn Geol 801, Shandong Prov Bur Geol & Mineral Resources, Jinan 250014, Peoples R China
[4] Nanjing Univ Aeronaut & Astronaut, Key Lab Radar Imaging & Microwave Photon, Minist Educ, Nanjing 210016, Peoples R China
[5] Changjiang River Sci Res Inst, Spatial Informat Technol Applicat Res Dept, Wuhan 430010, Peoples R China
[6] Shandong Engn Res Ctr Environm Protect & Remediat, Jinan 250014, Peoples R China
关键词
Remote sensing; Earth; Artificial satellites; Sensors; Data integration; Spatial resolution; Interpolation; High frequency; Sensor fusion; Low-pass filters; Sensor applications; filtering; Landsat-8; remote sensing data; sensor data fusion; Sentinel-2; 8; OLI;
D O I
10.1109/LSENS.2024.3499361
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
<!--?Firstputimage type="graphicalabstract" lines="9" overhang="5.50cm" id="lsens.gagraphic-3499361.eps"?-->With the increasing demand for high temporal and spatial resolution multispectral data sequences, many studies have been carried out on fusion on Landsat-8 and Sentinel-2 sensor data. However, current fusion methods suffer from the loss of detailed spatial and spectral information. To address this problem, a Landsat-8 and Sentinel-2 data fusion with multiscale detailed information (MSDI) method is proposed. MSDI combines well the initial spatial prediction obtained from the Landsat-8 data at the target date and the detailed part extracted from the Sentinel-2 data at the reference date. Thin plate spline interpolation is implemented on the Landsat-8 data for upsampling. Smoothing-sharpening filter (SSIF) is employed to separate the high- and low-frequency components of data from the two sensors. The multiscale SSIF is then utilized to migrate the details from the Sentinel-2 data to the upsampled Landsat-8 data. Experiments at two sites confirm that the proposed MSDI method could efficiently generate Sentinel-2-like data with high spatial and spectral resolution.
引用
收藏
页数:4
相关论文
共 25 条
[1]   A Spatial and Temporal Nonlocal Filter-Based Data Fusion Method [J].
Cheng, Qing ;
Liu, Huiqing ;
Shen, Huanfeng ;
Wu, Penghai ;
Zhang, Liangpei .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017, 55 (08) :4476-4488
[2]   A Guided Edge-Aware Smoothing-Sharpening Filter Based on Patch Interpolation Model and Generalized Gamma Distribution [J].
Deng, Guang ;
Galetto, Fernando ;
Alnasrawi, Mukhalad ;
Waheed, Waseem .
IEEE OPEN JOURNAL OF SIGNAL PROCESSING, 2021, 2 :119-135
[3]   On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance [J].
Gao, Feng ;
Masek, Jeff ;
Schwaller, Matt ;
Hall, Forrest .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2006, 44 (08) :2207-2218
[4]  
Gokon Hideomi, 2020, Advances in the Human Side of Service Engineering. Proceedings of the AHFE 2020 Virtual Conference on The Human Side of Service Engineering. Advances in Intelligent Systems and Computing (AISC 1208), P271, DOI 10.1007/978-3-030-51057-2_38
[5]   A Review of Quality Metrics for Fused Image [J].
Jagalingam, P. ;
Hegde, Arkal Vittal .
INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE'15), 2015, 4 :133-142
[6]   Physics-Based Fusion of Sentinel-2 and Sentinel-3 for Higher Resolution Vegetation Monitoring [J].
Kallel, Abdelaziz ;
Dalla Mura, Mauro ;
Fakhfakh, Sana ;
Ben Romdhane, Najmeddine .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61
[7]   Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations [J].
Ke, Yinghai ;
Im, Jungho ;
Lee, Junghee ;
Gong, Huili ;
Ryu, Youngryel .
REMOTE SENSING OF ENVIRONMENT, 2015, 164 :298-313
[8]   Spatio-temporal fusion for remote sensing data: an overview and new benchmark [J].
Li, Jun ;
Li, Yunfei ;
He, Lin ;
Chen, Jin ;
Plaza, Antonio .
SCIENCE CHINA-INFORMATION SCIENCES, 2020, 63 (04)
[9]   Spatiotemporal Reflectance Fusion via Tensor Sparse Representation [J].
Peng, Yidong ;
Li, Weisheng ;
Luo, Xiaobo ;
Du, Jiao ;
Zhang, Xiayan ;
Gan, Yi ;
Gao, Xinbo .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
[10]   Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product [J].
Shao, Zhenfeng ;
Cai, Jiajun ;
Fu, Peng ;
Hu, Leiqiu ;
Liu, Tao .
REMOTE SENSING OF ENVIRONMENT, 2019, 235