气候变化领域集成服务门户(CCPortal): Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data

CCPortal

DOI	10.1016/j.rse.2020.111782
	Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data
	Kim M.; Kim H.-C.; Im J.; Lee S.; Han H.
发表日期	2020
ISSN	00344257
卷号	242
英文摘要	Landfast sea ice (fast ice) is an important feature prevalent around the Antarctic coast, which is affected by climate change and energy exchanges with the atmosphere and ocean. This study proposed a method for detection of the West Antarctic fast ice using the Advanced Land Observing Satellite Phased Array L-band SAR (ALOS PALSAR) images. The algorithm has combined image segmentation, image correlation analysis, and machine learning techniques (i.e., random forest (RF), extremely randomized trees (ERT), and logistic regression (LR)). We used SAR images with a baseline of 5 days that are not in the same orbit but overlap each other as overlaps between swaths in adjacent orbits are often available in the polar regions. The underlying assumption for the proposed fast ice detection algorithm is that fast ice regions in SAR images with a time interval of 5 days are highly correlated. The object-based approach proposed in this study was well suited to high-resolution SAR images in deriving spatially homogeneous fast ice regions. The image segmentation results using the optimized parameters showed a distinct difference in the backscatter temporal evolution between fast ice and pack ice regions. Correlation and STD of backscattering coefficients were found to be the most significant variables for the object-based fast ice detection from two temporally separated images. In overall, the quantitative and qualitative evaluation demonstrated that the algorithm was an effective approach to detect fast ice with high accuracies. The models well detected various fast ice regions in the West Antarctica but misclassified some objects. The misclassifications occurred toward the edge of fast ice regions with relatively rapid changes in backscattering between both data acquisitions. On the other hand, few fast ice objects were misclassified as uniform backscattering over time occurred by chance on very small objects far from the coast. Very old multi-year fast ice regions with high backscattered signals were also a source for some misclassifications. This may be due to the sensitivity of L-band to snow structure to some extent and a thinner ice over the region with either ice growth (no deformation) or closing (slight deformation) between both images. Heavy snow load on the ice could be another error source for some misclassification as well. The approach allowed for the reliable detection of fast ice regions by using L-band SAR images with a small local incidence angle difference. © 2020 Elsevier Inc.
英文关键词	ALOS PALSAR; L-band SAR; Landfast sea ice; Machine learning; Object correlation analysis
语种	英语
scopus关键词	Backscattering; Climate change; Correlation methods; Decision trees; Deformation; Image segmentation; Learning systems; Logistic regression; Machine learning; Object detection; Orbits; Random forests; Sea ice; Snow; Structural dynamics; Synthetic aperture radar; Advanced land observing satellites; ALOS PALSAR; Backscattering coefficients; Correlation analysis; L-band SAR; Land-fast; Machine learning techniques; Qualitative evaluations; Radar imaging; ALOS; climate change; correlation; machine learning; PALSAR; sea ice; temporal evolution; time series analysis; Antarctica; West Antarctica
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/179356
作者单位	School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea; Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, 76128, Germany; Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, 76128, Germany; Unit of Arctic Sea-Ice Prediction, Korea Polar Research Institute, Incheon, 21990, South Korea; Centre for Polar Observation and Modelling, Earth Sciences, University College London, London, WC1E 6BS, United Kingdom; Division of Geology & Geophysics, Kangwon National University, Chuncheon, Gangwon-do 24341, South Korea
推荐引用方式 GB/T 7714	Kim M.,Kim H.-C.,Im J.,et al. Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data[J],2020,242.
APA	Kim M.,Kim H.-C.,Im J.,Lee S.,&Han H..(2020).Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data.Remote Sensing of Environment,242.
MLA	Kim M.,et al."Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data".Remote Sensing of Environment 242(2020).