KOPRI Repository

Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data

Cited 0 time in wos
Cited 0 time in scopus
Metadata Downloads
Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data
Other Titles
ALOS PALSAR를 이용한 객체기반 서남극 고착빙 탐지
Kim, Miae
Kim, Hyun-cheol
Im, Jungho
Lee, Sanggyun
Han, Hyangsun
Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology
Landfast sea ice; L-band SAR; ALOS PALSAR; Object correlation analysis; Machine learning
Issue Date
Kim, Miae, et al. 2020. "Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data". REMOTE SENSING OF ENVIRONMENT, 242(1): 111782-111782.
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.
Appears in Collections  
2020-2020, Study on remote sensing for quantitative analysis of changes in the Arctic cryosphere (20-20) / Kim, Hyun-cheol (PE20080)
Files in This Item

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.