Coals and gangues are the main surface dump in t he coal mining process.Dynamic monitoring of those dumps using remote sensing t echnique is of great importance for mine environmental protection.In the tradit ional classification of visible and near-infrared remote sensing,part of the g angues might be misclassified as coal,due to the phenomenon of "different obje cts with the same spectrum",resulting in the decrease of classification accura cy.Thus,this study firstly acquired visible and near-infrared spectrums of 12coal samples and 115 gangue samples from Tiefa mining area in China.Most of th e gangue samples' spectrums are different from those of the coals,which can be easily distinguished.While,part of the gangues has the similar spectrum with c oal which results in misclassification.With an effort to improve image classifi cation accuracy,furthermore,we acquired the thermal infrared spectrum of the m isclassified gangue and the coal samples.The results indicate that there are di fferent spectral characteristics in thermal infrared band between coal and gangu e samples,which can be identified easily.Therefore,we proposed a method to se parate coal from gangue based on the combination of visible,near-infrared and thermal infrared spectrum.In the first palace,the method conducts measurement on the visible and near-infrared spectrums of all samples for the rough classif ication recurring to the MAO model.Next,the thermal infrared spectrums of the samples,mixed with gangue and coal are acquired,and the Spectral Absorption Ra tio(SAR) is utilized as the evaluation index for the second classification.The fused result of classification originates in the two steps above.The method is further verified by using external samples from Tiefa,Yanzhou,Shendong and Jia ngcang mining areas in China,whose results have demonstrated that the method ha s higher accuracy than that of the traditional classification method based on vi sible and near-infrared spectrum features.The research results indicates that the conjoint analytical method involving multiple spectrums can solve the phenom enon of "different objects with the same spectrum" in a single band,effective ly,which will be of great referential significance in the field of terrain clas sification based on remote sensing technique.%煤与矸石是矿山采煤过程中主要固体堆放物,对其进行遥感动态监测是矿山环境保护的重要需求.由于煤与部分矸石存在"异物同谱"现象,在使用传统的可见-近红外遥感分类时,往往将部分矸石划分为煤,导致遥感分类精度降低.首先对铁法矿区的12个煤样本和115个矸石样本进行可见-近红外光谱测试,发现绝大部分矸石样品的光谱与煤差异很大,二者易于区分,但有部分矸石与煤样本存在"异物同谱"现象.为进一步对矸石与煤区分,测试了混分样本的热红外光谱,发现二者存在明显的光谱差异,利用热红外光谱特征可以将其区分开来.在此基础上,提出了基于可见-近红外和热红外光谱联合分析的煤与矸石区分方法.该方法首先对所有样本进行可见-近红外光谱测试,利用Mao模型进行第一步分类识别;其次对煤与矸石混分的样品进行热红外光谱测试,利用光谱吸收比率SAR作为判别指标进行第二步分类,两步的分类结果为最终分类结果.该方法在铁法、兖州、神东和木里矿区的验证结果表明,其具有很高的分类准确率,效果远好于单独基于可见-近红外光谱特征的分类方法.研究结果表明,利用多种光谱联合分析的方法可以解决单波段存在的"异物同谱"现象,对于地物遥感分类具有重要的借鉴意义.
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