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首页> 外文期刊>International journal of applied earth observation and geoinformation >Burnt area delineation from a uni-temporal perspective based on landsat TM imagery classification using Support Vector Machines
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Burnt area delineation from a uni-temporal perspective based on landsat TM imagery classification using Support Vector Machines

机译：使用支持向量机基于landsat TM影像分类从单时视角描述烧伤区域

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摘要

Information on burnt area is of critical importance in many applications as for example in assessing the disturbance of natural ecosystems due to a fire or in proving important information to policy makers on the land cover changes for establishing restoration policies of fire-affected regions. Such information is commonly obtained through remote sensing image thematic classification and a wide range of classifiers have been suggested for this purpose. The objectiveof the present study has been to investigate the use of Support Vector Machines (SVMs) classifier combined with multispectral Landsat TM image for obtaining burnt area mapping. As a case study a typical Mediterranean landscape in Greece was used, in which occurred one of the most devastating fires during the summer of 2007. Accuracy assessment was based on the classification overall statistical accuracy results and also on comparisons of the derived burnt area estimates versus validated estimates from the Risk-EOS Burnt Scar Mapping service. Results from the implementation of the SVM using diverse kernel functions showed an average overall classification accuracy of 95.87% and a mean kappa coefficient of 0.948, with the burnt area class always clearly separable from all the other classes used in the classification scheme. Total burnt area estimate computed from the SVM was also in close agreement with that from Risk-EOS (mean difference of less than 1%). Analysis also indicated that, at least for the studied here fire, the inclusion of the two middle infrared spectral bands TM5 and TM7 of TM sensor as well as the selection of the kernel function in SVM implementation have a negligible effect in both the overall classification performance and in the delineation of total burnt area. Overall, results exemplified the appropriateness of the spatial and spectral resolution of the Landsat TM imagery combined with the SVM in obtaining rapid and cost-effective post-fire analysis. This is of considerable scientific and practical value, given the present open access to the archived and new observations from this satellite radiometer globally.
机译：关于烧毁面积的信息在许多应用中至关重要，例如，在评估由于火灾引起的自然生态系统的干扰或向决策者提供有关土地覆盖变化的重要信息以建立受灾地区的恢复政策时。通常通过遥感影像主题分类获得这种信息，并且为此目的已经提出了各种各样的分类器。本研究的目的是研究将支持向量机（SVM）分类器与多光谱Landsat TM图像结合使用以获得燃烧区域图。作为案例研究，使用了希腊的典型地中海景观，该景观是2007年夏季发生的最具破坏性的大火之一。准确性评估基于分类的总体统计准确性结果，还基于得出的燃烧面积估算值与已通过Risk-EOS烧伤疤痕图服务进行了验证。使用各种核函数执行SVM的结果显示，平均总体分类准确度为95.87％，平均kappa系数为0.948，燃烧面积类别始终可以与分类方案中使用的所有其他类别明确地区分开。通过SVM计算得出的总燃烧面积估算值也与Risk-EOS的估算值非常吻合（平均差异小于1％）。分析还表明，至少对于此处研究的火灾，将TM传感器的两个中红外光谱带TM5和TM7包括在内，以及在SVM实现中选择核函数对总体分类性能的影响均可以忽略不计并描绘出总燃烧面积。总体而言，结果证明了Landsat TM影像与SVM结合使用的空间和光谱分辨率在获得快速且经济高效的射击后分析中的适当性。鉴于目前可以从全球范围内对该卫星辐射计公开获取存档和新观测资料，因此具有相当大的科学和实用价值。

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