Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery
Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery
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DOI：
10.5194/nhess-12-2893-2012
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发表时间：
2012-09
影响因子：
4.6
通讯作者：
M. Lato;R. Frauenfelder;Y. Bühler
中科院分区：
文献类型：
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作者：
M. Lato;R. Frauenfelder;Y. Bühler
抽象。山区的雪崩对基础设施（公路、铁路、能源输送走廊）、个人财产（住宅）和娱乐场所以及在高山地区生活和移动的人们的生命构成重大威胁。雪崩的影响范围很广，从道路和铁路关闭造成的延误和经济损失、财产和基础设施的破坏到生命损失。今天的雪崩警报主要基于气象信息、积雪信息、实地观察、历史记录的雪崩事件以及经验和专家知识。利用甚高分辨率光学遥感图像自动识别雪崩的能力有可能有助于绘制准确、空间分布广泛、详细的雪崩易发区地图，并有助于在交通不便的地区建立过去雪崩事件的数据库。这将使决策者更好地了解这些地区雪崩的频率和规模分布情况。我们使用了一个面向对象的图像解释方法，采用分割和分类方法，检测最近的雪崩存款VHR全色光学遥感图像。这就产生了雪崩存款图，可以与其他空间绘图和地形数据相结合。面向对象的方法已经过测试和验证，对人工生成的地图，雪崩的视觉识别和数字化。准确度（用户和生产商）均超过0.9，佣金误差小于0.05。未来的研究是针对广泛测试的算法所产生的数据由各种传感器和改进的算法在高噪声区域，以及雪崩路径的映射旁边的存款。
Abstract. Snow avalanches in mountainous areas pose a significant threat to infrastructure (roads, railways, energy transmission corridors), personal property (homes) and recreational areas as well as for lives of people living and moving in alpine terrain. The impacts of snow avalanches range from delays and financial loss through road and railway closures, destruction of property and infrastructure, to loss of life. Avalanche warnings today are mainly based on meteorological information, snow pack information, field observations, historically recorded avalanche events as well as experience and expert knowledge. The ability to automatically identify snow avalanches using Very High Resolution (VHR) optical remote sensing imagery has the potential to assist in the development of accurate, spatially widespread, detailed maps of zones prone to avalanches as well as to build up data bases of past avalanche events in poorly accessible regions. This would provide decision makers with improved knowledge of the frequency and size distributions of avalanches in such areas. We used an object–oriented image interpretation approach, which employs segmentation and classification methodologies, to detect recent snow avalanche deposits within VHR panchromatic optical remote sensing imagery. This produces avalanche deposit maps, which can be integrated with other spatial mapping and terrain data. The object-oriented approach has been tested and validated against manually generated maps in which avalanches are visually recognized and digitized. The accuracy (both users and producers) are over 0.9 with errors of commission less than 0.05. Future research is directed to widespread testing of the algorithm on data generated by various sensors and improvement of the algorithm in high noise regions as well as the mapping of avalanche paths alongside their deposits.