Abstract. The quantification of mineral resources refers to the fractional contribution of endmembers at the pixel level, namely, fraction cover mapping of mineralogy. Over a large area, the mineral deposit occurs generally in a limited number either on a host rock or any geologic structure. In remote sensing, the purity of mineral’s spectra is usually perturbed either because of the weathering effect or the compositional susceptibility, which may lead to a wrong fractional map of mineral endmembers. Having such physical disputes, the present paper establishes a fraction cover mapping model by incorporating the characterization of endmember variability, optimization model of endmember extraction (EE), and inverse model of abundance estimation. In this regard, a proposition of EE method was deployed, which comprises subproblems on the minimization of endmember variability by the alternating direction method. Next, the extracted endmembers were used to estimate abundances with the Hapke model by applying the fully constrained least-squares method. Experimenting on a synthetic image, both the qualitative analysis by correlation measure and quantitative analysis by statistical error measure were evaluated for the proposed fractional cover mapping model. Using airborne visible/infrared imaging spectrometer-next generation hyperspectral imagery, the fraction cover map of a validation area was justified first, then a distributed mapping of Jahazpur-mineralized belt was achieved by the MapReduce programming of the proposed model in Hadoop architecture.

中文翻译：

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基于交替方向方法的端元提取，用于印度贾哈兹布尔矿物学的分布式分数覆盖图

摘要。矿产资源的量化是指端元在像元层面的分数贡献，即矿物学的分数覆盖映射。在大面积上，矿床通常以有限的数量出现在主岩或任何地质结构上。在遥感中，由于风化效应或成分敏感性，矿物光谱的纯度通常会受到干扰，这可能导致矿物端元的错误分数图。面对这样的物理争议，本文结合端元变异性表征、端元提取（EE）优化模型和丰度估计逆模型，建立了分数覆盖映射模型。在这方面，部署了EE方法的提议，其中包括通过交替方向方法最小化端元变异性的子问题。接下来，通过应用完全约束的最小二乘法，使用提取的端元通过 Hapke 模型估计丰度。在合成图像上进行实验，对提出的分数覆盖映射模型进行了相关度量的定性分析和统计误差度量的定量分析。使用机载可见光/红外成像光谱仪-下一代高光谱图像，首先验证验证区域的分数覆盖图，然后通过Hadoop架构中提出的模型的MapReduce编程实现Jahazpur矿化带的分布式映射。通过应用完全约束的最小二乘法，提取的端元用于使用 Hapke 模型估计丰度。在合成图像上进行实验，对提出的分数覆盖映射模型进行了相关度量的定性分析和统计误差度量的定量分析。使用机载可见光/红外成像光谱仪-下一代高光谱图像，首先验证验证区域的分数覆盖图，然后通过Hadoop架构中提出的模型的MapReduce编程实现Jahazpur矿化带的分布式映射。通过应用完全约束的最小二乘法，提取的端元用于使用 Hapke 模型估计丰度。在合成图像上进行实验，对提出的分数覆盖映射模型进行了相关度量的定性分析和统计误差度量的定量分析。使用机载可见光/红外成像光谱仪-下一代高光谱图像，首先验证验证区域的分数覆盖图，然后通过Hadoop架构中提出的模型的MapReduce编程实现Jahazpur矿化带的分布式映射。对所提出的分数覆盖映射模型进行了相关性度量的定性分析和统计误差度量的定量分析。使用机载可见光/红外成像光谱仪-下一代高光谱图像，首先验证验证区域的分数覆盖图，然后通过Hadoop架构中提出的模型的MapReduce编程实现Jahazpur矿化带的分布式映射。对所提出的分数覆盖映射模型进行了相关性度量的定性分析和统计误差度量的定量分析。使用机载可见光/红外成像光谱仪-下一代高光谱图像，首先验证验证区域的分数覆盖图，然后通过Hadoop架构中提出的模型的MapReduce编程实现Jahazpur矿化带的分布式映射。