To address the class confusion caused by using only spectral features in image clustering for lithological recognition and classification based on multispectral- hyperspectral data, spectral-spatial features are used in multispectral image clustering. Two ways are adopted to include spatial information in clustering. First, multi-scale images are generated using discrete wavelet transform, and are successively clustered from coarse to fine scale images. Second, the spatial class membership probability is calculated using Markov random field model in different image scales, and the combined spectral-spatial class membership probability is then obtained. Finally, the final clustering results are obtained by combining the spectral-spatial clustering results of multiple scales. ASTER multispectral data were used for lithological mapping, and compared with the spectral clustering method based on Gaussian mixture model clustering (spectral clustering), the filtering after spectral clustering, and the spectral-spatial clustering on original image. The results show that the method of spectral-spatial clustering on multiscale image adopted obtained higher accuracies of lithological mapping than the above three comparative methods. Using spectral and spatial information at multi-scales in clustering is an effective method for lithological mapping. The method is applicable to lithological mapping in areas where training samples are difficult to obtain. 

针对现有基于多光谱–高光谱数据的岩性识别和分类研究中只利用光谱特征进行聚类, 容易产生类别混淆的问题, 利用光谱–空间特征进行多光谱图像的聚类。采取两种策略加入空间信息, 一是利用离散小波变换生成多尺度图像, 对不同尺度的图像依次进行聚类; 二是在各个尺度图像的聚类结果中, 利用马尔可夫随机场模型计算类别的空间概率, 得到类别的光谱–空间概率。综合多个尺度的光谱–空间聚类结果, 得到最终的聚类结果。利用 ASTER 多光谱数据进行岩性识别与分类, 并与基于高斯混合模型的光谱聚类结果、光谱聚类后滤波的结果和原始图像的光谱–空间聚类结果进行对比, 结果表明, 所采用的多尺度光谱–空间聚类方法可获得比上述3种方法更高精度的岩性分类结果, 说明综合利用光谱与空间信息进行多尺度的聚类是一种有效的岩性填图方法, 适用于较难获取地面参考样本地区的岩性填图。