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Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification

机译：中国精细空间分辨率高光谱卫星天宫一号在城市土地覆盖分类中的评价

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The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m) to EO-1 Hyperion (with a spatial resolution of 30 m). The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC), pixel-based support vector machine (PSVM), hybrid maximum likelihood classifier (HMLC), and hybrid support vector machine (HSVM) were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the hybrid classifier.
机译：中国高空间分辨率高光谱卫星天宫一号（TG-1）的成功发射为遥感卫星图像的应用开辟了新的可能性。 TG-1任务的主要目标之一是在局部和景观空间尺度上观察地表属性，以使用高光谱技术准确绘制城市土地覆盖图。这项研究试图通过将TG-1（空间分辨率为10 m）与EO-1 Hyperion（空间分辨率为30）进行比较，使用中国北京的现有数据评估TG-1数据集，以进行城市特征分析m）。首先分析了TG-1的光谱特征，从而找到了可用于区分城市地区的最佳高光谱波段。基于此，实现了基于像素的最大似然分类器（PMLC），基于像素的支持向量机（PSVM），混合最大似然分类器（HMLC）和混合支持向量机（HSVM），并在绘制城市土地覆盖类型的应用。混合分类器方法结合了基于像素的分类器和基于对象的分割方法，被证明是一种传统的基于像素的分类器的有效替代方案，可用于处理卫星高光谱数据，尤其是精细的空间分辨率数据。对于TG-1图像，获得的基于像素的城市分类的平均总体准确度为89.1％，而获得混合城市分类的平均总体准确度为91.8％。对于Hyperion影像，获得的基于像素的城市分类的平均总体准确度为85.9％，而获得的混合城市分类的平均总体准确度为86.7％。总的来说，可以得出结论，精细的空间分辨率卫星高光谱数据TG-1在描绘复杂的城市场景时很有希望，尤其是在使用适当的分类器（例如混合分类器）时。

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