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Land cover mapping using Sentinel-1 SAR and Landsat 8 imageries of Lagos State for 2017

机译:使用Sentinel-1 SAR和LANDSAT 8的LAGOS州2017年陆地覆盖映射

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For several years, Landsat imageries have been used for land cover mapping analysis. However, cloud cover constitutes a major obstacle to land cover classification in coastal tropical regions including Lagos State. In this work, a land cover appearance for Lagos State is examined using Sentinel-1 synthetic aperture radar (SAR) and Land Satellite 8 (Landsat 8) imageries. To this aim, a Sentinel-1 SAR dual-pol (VV+VH) Interferometric Wide swath mode (IW) data orbit for 2017 and a Landsat 8 Operational Land Imager (OLI) for 2017 over Lagos State were acquired and analysed. The Sentinel-1 imagery was calibrated and terrain corrected using a SRTM 3Sec DEM. Maximum likelihood classification algorithm was performed. A supervised pixel-based imagery classification to classify the dataset using training points selected from RGB combination of VV and VH polarizations was applied. Accuracy assessment was performed using test data collected from high-resolution imagery of Google Earth to determine the overall classification accuracy and Kappa coefficient. The Landsat 8 was orthorectified and maximum likelihood classification algorithm also performed. The results for Sentinel-1 include an RGB composite of the imagery, classified imagery, with overall accuracy calculated as 0.757, while the kappa value was evaluated to be about 0.719. Also, the?Landsat 8 includes a RBG composite of the imagery, classified imagery, but an overall accuracy of 0.908 and a kappa value of 0.876. It is concluded that Sentinel 1 SAR result has been effectively exploited for producing acceptable accurate land cover map of Lagos State with relevant advantages for areas with cloud cover. In addition,? the Landsat 8 result reported a high accuracy assessment values with finer visual land cover map appearance.
机译:几年来,Landsat成像已经用于陆地覆盖映射分析。然而,云覆盖构成了沿海热带地区覆盖覆盖分类的主要障碍,包括拉各斯州。在这项工作中,使用Sentinel-1合成孔径雷达(SAR)和陆地卫星8(Landsat 8)成像检验Lagos状态的土地覆盖外观。为了这个目的,一个前哨1 SAR双POL(VV + VH)和陆地卫星8运算陆地成像仪(OLI),用于在2017年拉各斯州获得并分析2017干涉宽幅模式(IW)的数据轨道。 Sentinel-1图像被校准和使用SRTM 3SEC DEM纠正了地形。执行最大似然分类算法。应用了用于使用从VV和VH偏振的RGB组合中选择的训练点来对数据集进行分类的监督基于像素的图像分类。使用从Google地球的高分辨率图像收集的测试数据进行准确性评估,以确定整体分类准确性和kappa系数。 LANDSAT 8是矫正性和最大似然分类算法。 Sentinel-1的结果包括图像的RGB复合图像,分类图像,总精度计算为0.757,而Kappa值评估为约0.719。此外,and?Landsat 8包括图像的RBG复合物,分类图像,但整体精度为0.908,kappa值为0.876。结论:哨兵1 SAR结果得到了有效的利用,用于与云覆盖的地区生产拉各斯州的可接受准确土地覆盖图与相关优势。此外,? Landsat 8结果报告了具有更精度的视觉陆地覆盖地图外观的高精度评估值。

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