Abstract 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.

中文翻译：

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使用时间序列 ALOS PALSAR 数据在西南极洲进行基于物体的陆上快速海冰检测

摘要 陆地快速海冰（fast ice）是南极沿岸地区普遍存在的重要特征，受气候变化和与大气和海洋能量交换的影响。本研究提出了一种利用先进陆地观测卫星相控阵 L 波段 SAR (ALOS PALSAR) 图像探测南极西部快冰的方法。该算法结合了图像分割、图像相关分析和机器学习技术（即随机森林（RF）、极端随机树（ERT）和逻辑回归（LR））。我们使用了基线为 5 天的 SAR 图像，这些图像不在同一轨道上，但由于相邻轨道的条带之间的重叠在极地地区经常可用，因此彼此重叠。所提出的快速冰检测算法的基本假设是，时间间隔为 5 天的 SAR 图像中的快速冰区域高度相关。本研究中提出的基于对象的方法非常适合用于推导空间均匀的快速冰区的高分辨率 SAR 图像。使用优化参数的图像分割结果显示快速冰区和浮冰区之间的反向散射时间演变存在明显差异。发现反向散射系数的相关性和 STD 是从两个时间分离的图像中进行基于对象的快速冰检测的最重要变量。总的来说，定量和定性评估表明，该算法是一种以高精度检测快速冰的有效方法。这些模型很好地检测了南极洲西部的各种快速冰区，但错误地分类了一些物体。错误分类发生在快速冰区的边缘，两次数据采集之间的反向散射变化相对较快。另一方面，很少有快速冰物体被错误分类为在远离海岸的非常小的物体上偶然发生随时间的均匀反向散射。具有高反向散射信号的非常古老的多年快速冰区也是一些错误分类的来源。这可能是由于 L 波段在某种程度上对雪结构的敏感性以及该区域上的冰较薄，两幅图像之间有冰生长（无变形）或闭合（轻微变形）。冰上的大雪负荷也可能是某些错误分类的另一个错误来源。