Results 1 to 10 of about 129 (100)
Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data [PDF]
Sensors, 2021 A red edge band is a sensitive spectral band of crops, which helps to improve the accuracy of crop classification. In view of the characteristics of GF-6 WFV data with multiple red edge bands, this paper took Hengshui City, Hebei Province, China, as the ...
Youfeng Zou
exaly +4 more sources
Assessment of Carbon Sequestration Capacity of E. ulmoides in Ruyang County and Its Ecological Suitability Zoning Based on Satellite Images of GF-6 [PDF]
Sensors, 2023 Eucommia ulmoides Oliver. (E. ulmoides) is a species of small tree native to China. It is a valuable medicinal herb that can be used to treat Alzheimer’s disease, diabetes, hypertension, and other diseases. In addition, E. ulmoides is a source of rubber.
Juan Wang +5 more
doaj +2 more sources
An Integrated Pixel-Level Reflectance Adjustment (IPRA) for Harmonizing GF-1/6 WFV and Sentinel-2 MSI Data [PDF]
SensorsThis study proposes an integrated pixel-level reflectance adjustment (IPRA) method using Sentinel-2 MSI as the reference to address radiometric discrepancies in GF-1/6 WFV imagery, particularly caused by sensor decay and geometric distortions.
Jianli Shi +5 more
doaj +2 more sources
Using GIS Remote Sensing Image Data for Wetland Monitoring and Environmental Simulation. [PDF]
Comput Intell Neurosci, 2022 Through a comprehensive theoretical basis and actual test analysis of the application system design and functional efficiency of the cloud platform, this paper puts forward an artificial intelligence environmental data monitoring and wetland environmental simulation method based on GIS remote sensing images.
Cheng Q, Dang CN.
europepmc +2 more sources
Remote Sensing, 2020 As a new satellite sensor of the GaoFen (GF) series, GF-6 Wide Field of View (WFV) with the resolution of 16 m has the characteristics of wide coverage, high-frequency imaging and has four new bands of two red-edge, yellow, and purple compared with GF-1 ...
Jingxian Yu +6 more
exaly +3 more sources
Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data
Remote Sensing, 2021 Time series of vegetation indices can be utilized to capture crop phenology information, and have been widely used in land cover and crop classification, phenological feature extraction, and planting structure monitoring.
Linlin Zhang, Maofan Zhao, Youfeng Zou
exaly +3 more sources
Time-Series Cross-Radiometric Calibration and Validation of GF-6/WFV Using Multi-Site
Remote SensingThe GaoFen6 (GF-6) satellite, equipped with a wide full-swath (WFV) sensor, offers high spatial resolution and extensive coverage, making it widely utilized in agricultural and forestry classification, land resource monitoring, and other fields. Accurate
Yaokai Liu, Jian Zeng, Qijin Han
exaly +3 more sources
EFFECTS OF GF-6 SATELLITE RED-EDGE BANDS ON PEANUT DROUGHT MONITORING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Red-edge band is an indicator band to describe the health status of crops. In order to explore the impact of the red-edge bands on the accuracy of agricultural drought monitoring, this study used GF-6 WFV and Landsat8 to calculate the Red-edge Normalized
Y. Zhang +7 more
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Cloud detection plays a significant role in optical imagery, but snow has similar spectral characteristics to clouds in visible and near-infrared bands.
Qian Zhang +8 more
doaj +1 more source
Improving Cloud/Snow Detection in Remote Sensing Image with Spatiotemporal Information Fusion
Security and Communication Networks, Volume 2022, Issue 1, 2022., 2022 The clouds and snow in optical remote sensing images always interfere with the interpretation of remote sensing images, which even makes an entire image unavailable. In general, the proportion of cloud/snow cover in remote sensing images needs to be clarified to improve the utilization of remote sensing images.
Jianfeng Wen +4 more
wiley +1 more source