Mapping snow avalanche debris by object-based classification in mountainous regions from Sentinel-1 images and causative indices
Mapping snow avalanche debris by object-based classification in mountainous regions from Sentinel-1 images and causative indices
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根据 Sentinel-1 图像和致病指数，通过基于对象的分类在山区绘制雪崩碎片图
DOI：
10.1016/j.catena.2021.105559
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发表时间：
2021-11
期刊：
影响因子：
6.2
通讯作者：
Li Lanhai
中科院分区：
文献类型：
--
作者：
Liu Yang;Chen Xi;Qiu Yubao;Hao Jiansheng;Yang Jinming;Li Lanhai
随着卫星观测数据集的快速发展，雪崩检测算法的准确性不如目视判读，限制了雪崩灾害管理。为了弥合这一差距，提出了更先进的机器学习来绘制雪崩碎片。这些技术使用Sentinel-1 SAR散射特性和实地观测，主成分分析（PCA），支持向量机（SVM），和逻辑回归（LR）在中国新疆天山西部范围。具体而言，雪崩碎片样本中的指标描述了时移变化，由上升和下降图像对的变化量化。然后，结合影响因素，在区域尺度上进行PCA-LR和PCA-SVM转换点监测。最后，雪崩碎片分布检测（13.92米）。结果表明：（1）图像对的上升或下降并不能提高雪崩碎片检测的准确性。虽然上升图像的结果超过下降图像的结果，但它低估了碎片的数量，误检率和误检率很高。(2)上升和下降的相邻图像对的合成结果是非常令人满意的雪崩碎片检测。虽然PCA-LR的结果勉强超过了PCA-SVM的结果，（CSILR1 = 86.38 vs.CSISVM1 = 83.06，PODLR1 = 98.90 vs.PODSVM1 = 95.37; CSILR 2 = 84.90对比CSISVM 2 = 81.53，以及PODLR 2 = 98.56对比PODSVM 2 = 94.15），这两个结果都高估了碎片的数量（FBLR 1 = 113.39 vs. FBSVM 1 = 110.19; FBLR 2 = 114.64 vs. FBSVM 2 = 109.64），具有低的未命中率和误检测率（FARLR 1 = 12.73 vs. FARSVM 1 = 13.44; FARLR 2 = 14.03 vs. FARSVM 1 = 14.13）。(3)由于SAR图像的局限性和厚雪造成的大规模深霜的错误信号，发生了雪崩碎片像素的错误和遗漏检测。使用多轨道、偏振和地形指数的高精度方法令人鼓舞，因为它们通过噪声过滤和斑点减少揭示了板状和槽状雪崩碎片。
With the rapid development of satellite observation datasets, avalanche detection algorithms are not as accurate as visual interpretation, limiting avalanche hazard management. To bridge this gap, more advanced machine learning is proposed to map snow avalanche debris. Those techniques use Sentinel-1 SAR scattering characteristics and field observations with principal component analysis (PCA), support vector machine (SVM), and logistic regression (LR) in the western range of the Tianshan Mountains of Xinjiang, China. Specifically, the indicators in the snow avalanche debris samples described the time-shift variations, quantified by the variations from the ascending and descending image pairs. Then, combined with the causative factors, PCA-LR and PCA-SVM transformed point-monitoring at the regional scale. Finally, the snow avalanche debris distribution was detected (13.92 m). It was found that: (1) The accuracy of snow avalanche debris detection was not enhanced by ascending or descending image pairs. Although the ascending image results outweigh the descending ones, it underestimated the amount of debris with high miss and false detection rates. (2) The composite results of the ascending and descending adjacent image pairs were highly satisfactory for snow avalanche debris detection. Although the PCA-LR results narrowly overtook those for PCA-SVM (CSILR1 = 86.38 vs. CSISVM1 = 83.06, PODLR1 = 98.90 vs. PODSVM1 = 95.37; CSILR2 = 84.90 vs. CSISVM2 = 81.53, and PODLR2 = 98.56 vs. PODSVM2 = 94.15), both results overestimated the debris amounts (FBLR1 = 113.39 vs. FBSVM1 = 110.19; and FBLR2 = 114.64 vs. FBSVM2 = 109.64), with low miss and false detection rates (FARLR1 = 12.73 vs. FARSVM1 = 13.44; FARLR2 = 14.03 vs. FARSVM1 = 14.13). (3) False and missed detection of avalanche debris pixels occurred due to the SAR images' limitations and an incorrect signal from the massive, deep frost caused by thick snow. The high-accuracy approach using multiple orbits, polarizations, and terrain indices was encouraging because they revealed slab-and groove-type avalanche debris from noise filtering and speckle reduction.
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影响因子：
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DOI：
10.20944/preprints201910.0341.v1
发表时间：
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期刊：
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