FVC-Net:高空间分辨率植被覆盖度的融合网络  

FVC-Net:A fusion network for producing fine spatial resolution fractional vegetation cover

作　　者：张智昊 王群明 丁欣宇 ZHANG Zhihao;WANG Qunming;DING Xinyu(College of Surveying and Geo-Informatics,Tongji University,Shanghai 200092,China)

机构地区：[1]同济大学测绘与地理信息学院,上海200092

出　　处：《遥感学报》2024年第12期3184-3196,共13页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:42222108,42171345)。

摘　　要：植被覆盖度(FVC)是描述地表植被分布的定量指标之一。通过遥感卫星(如Landsat和Sentinel-2)获取大尺度下的高空间分辨(如10 m级)FVC,能为全球生态系统研究提供重要基础数据。然而,由于云雾干扰以及卫星重返时间分辨率有限等问题,高空间分辨FVC在时域上存在大量缺失。本文考虑协同30 m Landsat 8和10 m Sentinel-2数据,实现二者在时域上的互补。为解决二者空间分辨率不一致的问题,本文提出了一种基于FVC-Net的深度学习方法,通过融合10 m Sentinel-2归一化植被指数(NDVI)数据,将30 m Landsat FVC降尺度至10 m。FVC-Net方法构建双分支结构下的通道注意力模块用于FVC和NDVI的多尺度特征采集与融合,随后利用空间注意力模块将选择的特征进行细节增强,以有效描述不同获取时间下的10 m NDVI与30 m FVC之间的非线性映射关系。实验中,与4种典型非深度学习方法和4种深度学习方法相比,FVC-Net获得了更高精度的融合结果。FVC-Net有望应用于全球尺度下的30 m Landsat FVC产品的降尺度,为相关领域研究提供更为精细的数据支撑。Fractional Vegetation Cover(FVC)is defined as the percentage of the vertical projected area of vegetation to the total area of the projected subsurface.It is an important indicator to characterize the spatial distribution of vegetation on the land surface,which plays an essential role in quantifying the capacity of terrestrial ecosystems for carbon sequestration.Remote sensing satellites(such as Landsat and Sentinel-2)can acquire fine spatial resolution FVC data at the 10 m level,which are crucial sources for studies on the global ecosystem.However,a large amount of fine spatial resolution FVC data are unavailable in the temporal domain due to the relatively coarse temporal resolution of satellites,coupled with cloud contamination.This study considers the combination of 30 m Landsat 8 and 10 m Sentinel-2 to increase the temporal frequency of 10 m FVC data to obtain vegetation information timely.A deep learning-based method called FVC-Net was proposed to address the difference in spatial resolution.FVC-Net fuses 30 m Landsat FVC with the 10 m Sentinel-2 Normalized Difference Vegetation Index(NDVI)directly to produce 10 m Landsat FVC.Specifically,a two-branch network based on the multiscale attention mechanism is designed.In this network,channel enhancement blocks are used in FVC and NDVI branches for feature extraction and fusion.Then,the spatial attention blocks are employed to increase the spatial details of the fused FVC features.The scheme designed with FVC-Net can help to characterize the nonlinear relationship between 10 m NDVI and 30 m FVC.In the experiments,the proposed FVC-Net method was validated based on three regions selected in the urban area in Shanghai,China.FVC-Net was compared with four typical non-deep learning-based(i.e.,HPF,Indusion,SFIM and ATPRK)and four deep learning-based(i.e.,PanNet,PNN,HPGAN and NDVI-Net)fusion methods.Both visual and quantitative evaluation reveals that:1)in non-deep learning-based methods,ATPRK is more accurate than the other three methods;2)the results of the deep lear

关 键 词：遥感 植被覆盖度 (FVC) 归一化植被指数 (NDVI) 深度学习 降尺度 数据融合 

分 类 号：P2[天文地球—测绘科学与技术]