Abstract Due to the fragmented compositional structure of urban scenes, many pixels are mixtures of multiple materials even in high spatial resolution airborne hyperspectral data. In the past ten years, sparse regression based spectral unmixing methods have achieved some noticeable results. Recently, Chen et al. proposed a jointly sparse spectral mixture analysis model for urban mapping. Their model has a high computational load, however, and wrongly detects a water component in residential areas due to the spectral confusion between water, shadow and other low-albedo land cover materials. In this paper, we propose to exclude water from the spectral mixture analysis in urban scenes. In order to decrease the computational load of Chen et al.’s approach, we propose a fast jointly sparse unmixing method. Our experiments demonstrate that the proposed method obtains a slightly degraded result but has a much lower computational load. It is fourteen times faster than their method, and only requires about one-ninth of the memory. A parallel implementation of the proposed method shows its potential to be applied in practical applications, especially in resource-constrained computational environments.

中文翻译：

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使用快速联合稀疏光谱混合分析方法从机载高光谱图像绘制城市土地覆盖图

摘要 由于城市场景的碎片化组成结构，即使在高空间分辨率的机载高光谱数据中，许多像素也是多种材料的混合。近十年来，基于稀疏回归的光谱解混方法取得了一些显着的成果。最近，陈等人。提出了一种用于城市测绘的联合稀疏光谱混合分析模型。然而，他们的模型具有很高的计算量，并且由于水、阴影和其他低反照率土地覆盖材料之间的光谱混淆，错误地检测到住宅区中的水成分。在本文中，我们建议从城市场景中的光谱混合分析中排除水。为了减少 Chen 等人的方法的计算量，我们提出了一种快速联合稀疏解混合方法。我们的实验表明，所提出的方法获得的结果略有下降，但计算量要低得多。它比他们的方法快十四倍，并且只需要大约九分之一的内存。所提出方法的并行实现显示了其在实际应用中的应用潜力，尤其是在资源受限的计算环境中。