基于SREM 融合数据的矿物蚀变信息提取
Mineral Alteration Information Extraction Based on SREM Fusion Data

目前卫星高光谱数据幅宽小,难以进行大面积矿物填图应用.本文探讨了基于多光谱图像光谱分辨率增强方法(spectral resolutionenhancementmethod,SREM)融合算法,将Hyperion窄幅高光谱和先进星载热发射和反射辐射仪(advancedspacebornethermalemissionandreflectionradiometer, ASTER)宽幅多光谱数据进行融合,获得宽幅高光谱数据,从而进行矿物蚀 变信息提取的方法和流程.结果表明:(1)原始ASTER数据仅能识别出Al-OH 基团,Mg-OH 基团和Fe3+ 离子基团3种矿物, SREM 方法能够识别出高岭石、伊利石、绢云母、绿泥石和黄钾铁矾5种蚀变矿物;(2)融合图像矿物提取结果与原始ASTER 和Hyperion图像的相对精度分别达到90.56%和92.85%;(3)其中绢云母、伊利石、高岭石与Al-OH 基团,黄钾铁矾与Fe3+ 离子,绿泥石与Mg-OH 基团出露区域基本一致.SREM 融合数据具有幅宽大和光谱分辨率高的特点,提高了矿物蚀变信息解 译精度,该方法对大面积矿物填图具有示范作用.
The current sate11ite hyperspectra1 data with narrow swath has been restricted in the use of large area minera1 map- ping. The spectra1 resolution enhancement method(SREM) which restructures alarge width hyperspectra1 data with Hyperion (narrow swath hyperspecta1 data) and advanced spaceborne therma1 emission and reflection radiometer(ASTER) (wide swath multi-spectraldata) is discussed in the paper. The results show that: (1) only three kinds of minerals(A1-0H, Mg-0H and Fe3+) are detected in ASTER data, while five kinds of minerals(kaolinite, i11ite, ch1orite, sericite and jarosite) are identified in fusion hyperspectra1 data; (2) compared with the minerals of fusion data with ASTER and Hyperion data, the overa11 accu- racy is respectively90.56% and92.85%; (3) sericite, i11ite and kaolinite appear at the region of A1-0H, jarosite shows up at the area of Fe3+ and ch1orite shows up at Mg-0H region. The SREM fusion data with high spectra1 resolution can improve the mincra1 altcration cxtraction accuracy which makcs a good cxamplc forlargc arca mincra1 mapping