Abstract Understanding the urban environments and their spatio-temporal behavior is necessary for local and regional planning along with environmental management. For monitoring and analyzing the urban environment, remote sensing imagery has been widely used due to its ability for repetitive coverage over large geographical areas. Compared with conventional per-pixel and sub-pixel analysis of remote sensing imagery, spectral indices have noticeable advantages because of their easy implementation and fast execution. However, most of the spectral indices are designed for multispectral imagery to extract only one land cover class, and confusion between other land cover classes still persists. This research explores the most significant spectral bands in AVIRIS-NG hyperspectral imagery for detection of built-up surfaces and its subclasses i.e. roads and roofs. Further, this study utilizes existing built-up indices for detection of urban built-up surfaces in the first level followed by its subcategories in the second level. Finally, a separability analysis between spectrally mixed urban land cover classes using various measures is also addressed. Results of the analysis indicate that BSI, NBI, and BAEI can prove to be effective for extraction of built-up surfaces with an overall accuracy (OA) of 93.89%, 90.11%, and 85.15%, respectively. Further, REI with OA of 94.40% appears to be suitable for extraction of road surfaces while NBAI with 95% OA can prove its efficacy for extraction of rooftops in AVIRIS-NG imagery. It also concludes that, for aforesaid indices, built-up surfaces (Level-1 and 2) can be effectively separated from the bare soil in hyperspectral imagery with slight confusion between road and roof surfaces.

中文翻译：

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使用现有建筑指数提取城市建筑表面及其子类，并在 AVIRIS-NG 图像中对光谱混合类进行可分离性分析

摘要 了解城市环境及其时空行为对于地方和区域规划以及环境管理是必要的。对于城市环境的监测和分析，遥感影像因其能够重复覆盖大面积地理区域而被广泛使用。与传统的遥感影像逐像素和子像素分析相比，光谱指数具有明显的优势，因为它们易于实现和快速执行。然而，大多数光谱指数是为多光谱影像设计的，仅提取一种土地覆盖类别，其他土地覆盖类别之间的混淆仍然存在。本研究探索了 AVIRIS-NG 高光谱图像中最重要的光谱带，用于检测构造表面及其子类，即 道路和屋顶。此外，本研究利用现有的建成指数来检测第一级的城市建成区，然后是第二级的子类别。最后，还讨论了使用各种措施的光谱混合城市土地覆盖类别之间的可分离性分析。分析结果表明，BSI、NBI 和 BAEI 可以证明是有效提取积垢的方法，整体精度 (OA) 分别为 93.89%、90.11% 和 85.15%。此外，具有 94.40% OA 的 REI 似乎适合提取路面，而具有 95% OA 的 NBAI 可以证明其在 AVIRIS-NG 图像中提取屋顶的有效性。它还得出结论，对于上述指数，