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首页> 外文期刊>Journal of Volcanology and Geothermal Research >Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image
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Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image

机译：测试随机森林分类以识别熔岩流并在单个Landsat 8图像上绘制年龄组图

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摘要

Mapping lava flows using satellite images is an important application of remote sensing in volcanology. Several volcanoes have been mapped through remote sensing using a wide range of data, from optical to thermal infrared and radar images, using techniques such as manual mapping, supervised/unsupervised classification, and elevation subtraction. So far, spectral-based mapping applications mainly focus on the use of traditional pixel-based classifiers, without much investigation into the added value of object-based approaches and into advantages of using machine learning algorithms. In this study, Nyamuragira, characterized by a series of >20 overlapping lava flows erupted over the last century, was used as a case study. The random forest classifier was tested to map lava flows based on pixels and objects. Image classification was conducted for the 20 individual flows and for 8 groups of flows of similar age using a Landsat 8 image and a DEM of the volcano, both at 30-meter spatial resolution. Results show that object-based classification produces maps with continuous and homogeneous lava surfaces, in agreement with the physical characteristics of lava flows, while lava flows mapped through the pixel-based classification are heterogeneous and fragmented including much "salt and pepper noise". In terms of accuracy, both pixel-based and object-based classification performs well but the former results in higher accuracies than the latter except for mapping lava flow age groups without using topographic features. It is concluded that despite spectral similarity, lava flows of contrasting age can be well discriminated and mapped by means of image classification. The classification approach demonstrated in this study only requires easily accessible image data and can be applied to other volcanoes as well if there is sufficient information to calibrate the mapping. (C) 2017 Elsevier B.V. All rights reserved.
机译：利用卫星图像绘制熔岩流图是遥感在火山学中的重要应用。几座火山已经通过遥感进行了测绘，使用了从光学到热红外和雷达图像的广泛数据，并使用了诸如手工测绘，有监督/无监督分类和海拔减法等技术。到目前为止，基于频谱的地图绘制应用程序主要集中在传统的基于像素的分类器的使用上，而没有对基于对象的方法的附加值以及使用机器学习算法的优势进行过多研究。在这项研究中，Nyamuragira以案例研究为特征，其特征是在上个世纪爆发了一系列＞ 20个重叠的熔岩流。测试了随机森林分类器，以根据像素和对象映射熔岩流。使用Landsat 8图像和火山DEM对20条单独的流和8组年龄相似的流进行了图像分类，两者的空间分辨率均为30米。结果表明，基于对象的分类生成的熔岩表面连续且均质，与熔岩流的物理特征相符，而通过基于像素的分类映射的熔岩流则是异质且分散的，其中包括很多“盐和胡椒噪声”。在准确性方面，基于像素的分类和基于对象的分类均表现良好，但前者比后者具有更高的准确性，除了在不使用地形特征的情况下映射熔岩流年龄组的情况下。结论是，尽管光谱相似，但可以通过图像分类很好地区分和绘制对比年龄的熔岩流。在这项研究中演示的分类方法仅需要易于访问的图像数据，并且如果有足够的信息可以校准映射，则也可以将其应用于其他火山。 （C）2017 Elsevier B.V.保留所有权利。

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