气候变化领域集成服务门户(CCPortal): CNN-based burned area mapping using radar and optical data

CCPortal

DOI	10.1016/j.rse.2021.112468
	CNN-based burned area mapping using radar and optical data
	Belenguer-Plomer M.A.; Tanase M.A.; Chuvieco E.; Bovolo F.
发表日期	2021
ISSN	00344257
卷号	260
英文摘要	In this paper, we present an in-depth analysis of the use of convolutional neural networks (CNN), a deep learning method widely applied in remote sensing-based studies in recent years, for burned area (BA) mapping combining radar and optical datasets acquired by Sentinel-1 and Sentinel-2 on-board sensors, respectively. Combining active and passive datasets into a seamless wall-to-wall cloud cover independent mapping algorithm significantly improves existing methods based on either sensor type. Five areas were used to determine the optimum model settings and sensors integration, whereas five additional ones were utilised to validate the results. The optimum CNN dimension and data normalisation were conditioned by the observed land cover class and data type (i.e., optical or radar). Increasing network complexity (i.e., number of hidden layers) only resulted in rising computing time without any accuracy enhancement when mapping BA. The use of an optimally defined CNN within a joint active/passive data combination allowed for (i) BA mapping with similar or slightly higher accuracy to those achieved in previous approaches based on Sentinel-1 (Dice coefficient, DC of 0.57) or Sentinel-2 (DC 0.7) only and (ii) wall-to-wall mapping by eliminating information gaps due to cloud cover, typically observed for optical-based algorithms. © 2021 The Author(s)
英文关键词	Burned area mapping; Convolutional neural networks; Deep learning; SAR; Sentinel-1; Sentinel-2; Wildland fires
语种	英语
scopus关键词	Convolution; Convolutional neural networks; Deep neural networks; Remote sensing; Synthetic aperture radar; Burned-area mapping; Cloud cover; Convolutional neural network; Deep learning; Network-based; Optical-; SAR; Sentinel-1; Sentinel-2; Wildland fire; Conformal mapping
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178829
作者单位	Environmental Remote Sensing Research Group, Dep. of Geology, Geography and Environment, Universidad de Alcalá, Alcalá de Henares, 28801, Spain; Center for Information and Communication Technology, Fondazione Bruno Kessler, Trento, 38122, Italy
推荐引用方式 GB/T 7714	Belenguer-Plomer M.A.,Tanase M.A.,Chuvieco E.,et al. CNN-based burned area mapping using radar and optical data[J],2021,260.
APA	Belenguer-Plomer M.A.,Tanase M.A.,Chuvieco E.,&Bovolo F..(2021).CNN-based burned area mapping using radar and optical data.Remote Sensing of Environment,260.
MLA	Belenguer-Plomer M.A.,et al."CNN-based burned area mapping using radar and optical data".Remote Sensing of Environment 260(2021).