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摘要： 镁铁质―超镁铁质岩的遥感岩性识别研究一直是遥感岩石学领域的热点之一，其岩石信息对于岩浆型铜镍硫化物矿床的预测十分重要。提出一种综合利用Landsat 8、ASTER和PALSAR-2数据的镁铁质―超镁铁质岩石全谱段遥感识别模型：通过对野外采集岩石样品及光谱库中相关岩石矿物的可见光―热红外谱域的光谱测试与特性分析，结合岩石表面微波散射特性，应用特征空间，贝叶斯线性判别分析、逐步偏最小二乘回归分析等方法，创建镁铁质―超镁铁质岩性指数，并利用提出的岩性指数进行镁铁质―超镁铁质岩的初步提取，再经过基于贝叶斯决策理论的融合处理得到最终的镁铁质―超镁铁质岩石信息。结果表明:该模型能够对镁铁质―超镁铁质岩进行精准定位，识别精度达到94%以上。此外，根据遥感岩性、构造特征的解译与区内已知铜镍硫化物矿床的成矿岩体比较，推断出位于赤石山―小长山―中坡山一带以及小青山附近岩体具有一定的成矿潜力。 关键词： 全谱段遥感;  镁铁质―超镁铁质岩;  特征空间;  贝叶斯决策理论;  识别模型     Abstract: Lithological mapping of mafic-ultramafic rocks is a hotspot in remote sensing petrology, and this lithological information is significant for the prediction of magmatic Ni-Cu sulfide ore deposit. This paper proposed a full-spectrum remote sensing identification model based on Landsat-8, ASTER, and PALSAR-2 data to extract mafic-ultramafic rocks. Firstly, mafic-ultramafic rock indices were proposed by feature space model, Bayesian linear discriminant analysis, and stepwise partial least squares regression analysis, based on analysis of the laboratory reflectance and emissivity spectra of field rock samples measured by portable spectrometer and related rocks and minerals spectra from spectral library in the visible to thermal infrared region, combined with the microwave scattering properties of the rock surface. And then these mafic-ultramafic rock indices were used to preliminary obtain the mafic-ultramafic rock information. Finally, the final mafic-ultramafic rock information was obtained after multi-information fusion processing based on Bayesian decision theory. The results show that the full-spectrum remote sensing identification model can accurately locate mafic-ultramafic rocks, and the extraction accuracy of the mafic-ultramafic rock information is above 94%. Furthermore，Based on mafic-ultramafic rock extraction of this identification model and structure interpretation, mineral exploration prospect also infer around Chishishan-Xiaochangshan-Zhongposhan area and Xiaoqingshan area, comparing with metallogenic rock masses of known Ni-Cu sulfide ore deposits in study area.

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