高光谱图像在地物观测领域得到了广泛的应用。由于高光谱图像具有数据量大、波段间相关度高等特性，波段选择技术成为降低地物识别计算复杂度的重要方法。根据不同波段数据之间的非线性关系，提出了基于谱聚类(SC)的波段选择技术。该方法首先以波段图像为样本点生成近邻图和相似度矩阵，然后借助谱聚类方法将所有数据样本分成 k类，从中选择 k个代表波段参与后继的分类识别任务。实验数据表明，新方法减小了计算复杂度，提高了地物识别的精度。

Hyperspectral image has been widely used in land-cover classification. Due to huge amount of data and high correlation between bands, band selection technology is main method to reduce computational complexity. According to non-linear relation between band data, Spectral Clustering (SC) is imported to cluster and select band. In this method, neighbor graph and similarity matrix are generated from band image samples, then samples are divided into k clusters by spectral clustering algorithm. At last, k selected representative samples are generated and used in the subsequent classification and recognition task. Experimental results show that new method can reduce computational complexity and improve classification accuracy of land-cover classification.