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首页> 外文期刊>Geomorphology >Image enhancements of Landsat 8 (OLI) and SAR data for preliminary landslide identification and mapping applied to the central region of Kenya
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Image enhancements of Landsat 8 (OLI) and SAR data for preliminary landslide identification and mapping applied to the central region of Kenya

机译：Landsat 8（OLI）和SAR数据的图像增强，用于肯尼亚中部地区的初步滑坡识别和制图

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Image enhancements lead to improved performance and increased accuracy of feature extraction, recognition, identification, classification and hence change detection. This increases the utility of remote sensing to suit environmental applications and aid disaster monitoring of geohazards involving large areas. The main aim of this study was to compare the effect of image enhancement applied to synthetic aperture radar (SAR) data and Landsat 8 imagery in landslide identification and mapping. The methodology involved pre-processing Landsat 8 imagery, image co-registration, despeckling of the SAR data, after which Landsat 8 imagery was enhanced by Principal and Independent Component Analysis (PCA and ICA), a spectral index involving bands 7 and 4, and using a False Colour Composite (FCC) with the components bearing the most geologic information. The SAR data were processed using textural and edge filters, and computation of SAR incoherence. The enhanced spatial, textural and edge information from the SAR data was incorporated to the spectral information from Landsat 8 imagery during the knowledge based classification. The methodology was tested in the central highlands of Kenya, characterized by rugged terrain and frequent rainfall induced landslides. The results showed that the SAR data complemented Landsat 8 data which had enriched spectral information afforded by the FCC with enhanced geologic information. The SAR classification depicted landslides along the ridges and lineaments, important information lacking in the Landsat 8 image classification. The success of landslide identification and classification was attributed to the enhanced geologic features by spectral, textural and roughness properties. (C) 2017 Elsevier B.V. All rights reserved.
机译：图像增强可以提高性能，并提高特征提取，识别，识别，分类以及更改检测的准确性。这增加了遥感的实用性，以适应环境应用并有助于对涉及大面积区域的地质灾害进行灾害监测。这项研究的主要目的是比较应用于合成孔径雷达（SAR）数据和Landsat 8影像的图像增强在滑坡识别和制图中的作用。该方法包括预处理Landsat 8图像，图像共配准，SAR数据去斑，然后通过主成分分析和独立分量分析（PCA和ICA）增强了Landsat 8图像，该光谱指数涉及7和4波段，以及使用假彩色复合材料（FCC），且组成部分具有最多的地质信息。使用纹理和边缘滤波器处理SAR数据，并计算SAR不相干性。在基于知识的分类过程中，来自SAR数据的增强的空间，纹理和边缘信息被合并到Landsat 8影像的光谱信息中。该方法在肯尼亚中部高地进行了测试，其特征是崎terrain的地形和频繁的降雨诱发的滑坡。结果表明，SAR数据是对Landsat 8数据的补充，这些数据丰富了FCC提供的光谱信息并具有增强的地质信息。 SAR分类描述了沿着山脊和地形的滑坡，Landsat 8影像分类中缺少重要信息。滑坡识别和分类的成功归因于光谱，纹理和粗糙度特性增强的地质特征。 （C）2017 Elsevier B.V.保留所有权利。

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