蚀变矿物在可见光-短波红外波段范围具有光谱特征，是利用卫星遥感图像提取蚀变信息的重要理论依据。由于遥感数据源和可用技术方法的局限，小规模蚀变信息难以有效识别。本项目基于WorldView-3、JPL光谱库及实测光谱数据，在分析不同分辨率下蚀变像元光谱特性变化规律、选取训练样本及确定模型最优参数的基础上，建立优化极限学习机（OP-ELM）模型对各类型蚀变规模进行识别。进一步研究空间分辨率变化对不同规模蚀变提取精度的影响，量化各空间分辨率下可辨识蚀变的最小规模，提出适用于小规模蚀变信息提取的有效方法。研究结果可为小规模遥感蚀变提取、不同比例尺遥感找矿工作中数据分辨率选择提供理论基础与数据支持。

The spectral differences from visible through the short wave infrared (SWIR) region of the electromagnetic spectrum for alteration minerals are the theoretical basis of the studies for alteration mineral mapping using remote sensing data. Limited by the remote sensing data and the usable methods, alteration minerals with small scale are difficult to identify. This project aims to study the effective method for identifying alteration zones with small scale. The Mingjingou gold deposit and the surrounding area were set as the study area. The WorldView-3 images, field measured spectra and the spectra library were selected for the study. First, based on the spatial resolution and spectral resolution of WorldView-3, the characters of endmember spectra of different alteration types were analyzed. Second, the training samples were selected by overlaying the results of three spectral matching methods. Third, an optimally pruned extreme learning machine was trained and built for distinguishing the scale of alteration zones. Fourth, the OP-ELM medal was improved after the field validation. Finally, the relationship between the changing spatial resolution and the identification ability for the different scale of alteration zones was quantified. The utilized methods and results could provide a theoretical basis and data support for mapping small scale alteration zones and the spatial resolution selection of remote sensing data for remote sensing exploration with different scale.