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首页> 外文期刊>Remote Sensing >Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada
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Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada

机译：利用极化合成孔径X和C波段雷达（PolSAR）和Landsat 8多光谱图像对北极苔原环境进行土地覆盖特征表征和分类—加拿大理查兹岛

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摘要

In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques.
机译：在这项工作中，结合多光谱Landsat 8数据检查了双极化TerraSAR-X（HH / VV）和四极化Radarsat-2的极化合成孔径雷达（PolSAR）数据的潜力，以对苔原进行无监督和有监督的分类涵盖加拿大理查兹岛的类型。使用在三个野外工作期间收集的参考数据分析了分类精度以及后向散射和反射特性，其中包括原位数据和高分辨率机载摄影。光学数据为土地覆被分类提供了可接受的初始精度。通过结合PolSAR和光学数据可以提高整体精度，对于五个苔原土地覆盖物，无监督（Landsat 8和TerraSAR-X）的最高准确度达到71％，有监督分类（Landsat 8和Radarsat-2）的最高准确率高达87％类型。双极化和四极化数据的分解特征显示出对非植被基质（主要是表面散射）和湿地植被（主要是双反弹和体积散射）的高灵敏度。这些类别具有使用无监督分类技术自动检测的潜力。

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