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首页> 外文期刊>International journal of remote sensing >Wetland classification using Radarsat-2 SAR quad-polarization and Landsat-8 OLI spectral response data: a case study in the Hudson Bay Lowlands Ecoregion
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Wetland classification using Radarsat-2 SAR quad-polarization and Landsat-8 OLI spectral response data: a case study in the Hudson Bay Lowlands Ecoregion

机译：使用Radarsat-2 SAR四极化和Landsat-8 OLI光谱响应数据进行湿地分类：以哈德逊湾低地生态区为例

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摘要

Circumboreal Canadian bogs and fens distinguished by differences in soils, hydrology, vegetation and morphological features were classified using combinations of Radarsat-2 synthetic aperture radar (SAR) quad-polarization data and Landsat-8 Operational Land Imager (OLI) spectral response patterns. Separate classifications were conducted using a traditional pixel-based maximum likelihood classifer and a machine learning algorithm following an object-based image analysis (OBIA). This study focused on two wetland classes with extensive coverage in the area (bog and fen). In the pixel-based maximum likelihood classification, accuracy increased from approximately 69% user's accuracy and 79% producer's accuracy using Radarsat-2 SAR data alone to approximately 80% user's accuracy and 87% producer's accuracy using Landsat-8 OLI data alone. Use of the Radarsat-2 SAR and Landsat-8 OLI data following principal components analysis (PCA) data fusion did not result in higher pixel-based maximum likelihood classification accuracy. In the object-based machine learning classification, higher bog and fen class accuracies were obtained when using Radarsat-2 and Landsat OLI data individually compared to the equivalent pixel-based classification. Subsequently, a PCA-data fusion product outperformed the individual bands of the Radarsat-2 and Landsat-8 imagery in object-based classification. Greater than 90% producer's accuracy was obtained. The margin of error (MOE) was less than 5% in all classifications reported here. Further research will examine alternative data fusion techniques and the addition of Radarsat-2 SAR interferometric digital elevation model (DEM)-based geomorphometrics in object-based classification of different morphological types of bogs and fens.
机译：使用结合了Radarsat-2合成孔径雷达（SAR）四极化数据和Landsat-8实用陆地成像仪（OLI）光谱响应模式，对以土壤，水文，植被和形态特征差异为特征的加拿大周围沼泽和类进行了分类。使用传统的基于像素的最大似然分类器和基于对象的图像分析（OBIA）之后的机器学习算法进行单独的分类。这项研究的重点是在该地区（沼泽和芬）有广泛覆盖的两个湿地类别。在基于像素的最大似然分类中，准确度从仅使用Radarsat-2 SAR数据的大约69％用户准确度和79％生产者准确度提高到仅使用Landsat-8 OLI数据的大约80％用户准确度和87％生产者准确度。在主成分分析（PCA）数据融合之后使用Radarsat-2 SAR和Landsat-8 OLI数据不会导致更高的基于像素的最大似然分类精度。在基于对象的机器学习分类中，与等效的基于像素的分类相比，分别使用Radarsat-2和Landsat OLI数据可获得更高的沼泽和类精度。随后，在基于对象的分类中，PCA数据融合产品的性能优于Radarsat-2和Landsat-8影像的各个波段。获得了超过90％的生产者精度。在此报告的所有分类中，误差幅度（MOE）均小于5％。进一步的研究将研究替代数据融合技术，以及基于Radarsat-2 SAR干涉数字高程模型（DEM）的地貌法在不同形态类型的沼泽和虫的基于对象的分类中的应用。

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