基于优化K-P-Means解混方法的高光谱图像矿物识别  

Mineral identification from hyperspectral images based on the optimized K-P-Means unmixing method

作　　者：孙肖 徐林林 王晓阳 田野 王伟[1] 张中跃 SUN Xiao;XU Linlin;WANG Xiaoyang;TIAN Ye;WANG Wei;ZHANG Zhongyue(Langfang Natural Resources Comprehensive Survey Center,China Geological Survey,Langfang 065000,China;School of Land Science and Technology,China University of Geosciences(Beijing),Beijing 100083,China)

机构地区：[1]中国地质调查局廊坊自然资源综合调查中心,廊坊065000 [2]中国地质大学(北京)土地科学技术学院,北京100083

出　　处：《自然资源遥感》2022年第3期43-49,共7页Remote Sensing for Natural Resources

基　　金：中国地质调查局项目“辽阳市多要素城市地质调查”(编号:DD20191025)资助。

摘　　要：有效的高光谱混合像元分解方法可以提高矿物信息提取的精度。为进一步研究高光谱混合像元分解方法,采用线性光谱混合模型解释高光谱图像的成像机制,用不同矿物端元的线性组合表达混合像元。在最大似然估计的框架下,利用期望最大算法对混合像元的端元和丰度进行估计。针对端元提取易受异常值影响的问题,提出基于随机抽样一致算法的稳健的K-P-Means算法,优化端元提取过程。利用光谱角度距离和光谱信息散度评价估计端元和真实端元的一致性;利用结构相似性和峰值信噪比衡量估计的丰度和端元重新获得的图像与原图像的相似性。多种仿真数据指标显示,该优化模型可以获得精度较高的端元和丰度估计结果。通过将提取的端元与美国地质调查局光谱库提供的矿物波谱曲线进行匹配来确定矿物类型,真实数据采用内华达州铜矿区的AVIRIS高光谱传感器Cuprite数据集。矿物提取结果显示,该模型对绿泥石等8种类别的主要矿物识别结果较好,矿物聚集性明显,与实际情况一致。该方法不仅可以较好地提取矿物信息,而且可以有效抵制噪声的影响。An effective unmixing method of hyperspectral mixed pixels can improve the precision of mineral information extraction.To further study such unmixing methods,this study explained the imaging mechanism of hyperspectral images using a linear spectral mixing model.The linear combinations of different mineral endmembers were used to express mixed pixels.The expected maximum(EM)algorithm was used to estimate the endmembers and abundance of mixed pixels under the framework of maximum likelihood estimation.A robust K-P-Means algorithm based on a random sampling consensus algorithm was proposed to improve the endmember optimization process,aiming to resist the impacts of anomalies on endmember extraction.The spectral angular distance and the spectral information divergence were used to assess the consistency between the estimated endmembers and the real endmembers.To obtain the similarity between the image and the original image,the structural similarity and the peak signal-to-noise ratio were used to measure the estimated abundance and endmembers.Various simulation data indicators show that the optimized model can obtain more precise estimations of endmembers and abundance.The mineral types were determined by matching the extracted endmembers with the mineral spectrum curves provided by the USGS spectral library.The actual data originated from the Cuprite data set of the AVIRIS hyperspectral sensor for the Nevada copper mining area.The results of mineral extraction showed that the model proposed in this study yielded satisfactory recognition results for eight types of main minerals including chlorite,which showed significant mineral aggregation and were consistent with the actual situation.Therefore,the method proposed in this study can extract precise mineral information while effectively resisting the impacts of noise.

关 键 词：矿物 高光谱图像 端元 解混 稳健 

分 类 号：P962[天文地球—自然地理学]