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Automatic Extraction of Lakes on the Qinghai-Tibet Plateau from Sentinel-1 SAR Images

机译:从Sentinel-1 SAR影像中自动提取青藏高原湖泊

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As a link between the cryosphere, hydrosphere, biosphere and the atmosphere, lakes in the Qinghai-Tibet Plateau can indicate global climate change. However, due to the remoteness and inaccessible, the measured lake data is challenging to obtain. In this paper, a new automatic water extraction method for Qinghai-Tibet Plateau lakes from Sentinel-1 SAR images has been proposed. For solving the problem that conventional methods unable to satisfy the description of large-scale statistical characteristics, we present an easy-to-implement classification method combining random forest (RF) and multi-features including a new statistical feature called object-based generalized gamma distribution (OGΓD). According to several experiments, we demonstrate that our method is capable of accurately extracting lakes, performing an overall accuracy of 98.60% and 97.06% in Zonag Lake and Yanhu Lake respectively, without the requirement of too much preprocessing or postprocessing steps at the same time.
机译:作为冰冻圈,水圈,生物圈和大气之间的联系,青藏高原的湖泊可以指示全球气候变化。但是,由于地处偏僻且交通不便,因此很难获得所测得的湖泊数据。本文提出了一种从Sentinel-1 SAR图像中提取青藏高原湖泊水的自动方法。为了解决传统方法无法满足大规模统计特征描述的问题,我们提出了一种易于实现的分类方法,将随机森林(RF)和多特征相结合,其中包括一种新的统计特征,称为基于对象的广义伽玛分布(OGΓD)。根据几个实验,我们证明了该方法能够准确地提取湖泊,在Zonag湖和Yanhu湖中的总准确度分别为98.60%和97.06%,而无需同时进行太多的预处理或后处理步骤。

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