基于无监督特征选择的高光谱图像分类方法

在高光谱图像分类中,丰富的数据提升了其地物 识别能力。然而,由于样本特 征数大且有标记训练样本点少,导致“维度灾难”问题。本文提出一种基于无监督特征选择 的高光谱图像分类方 法,该方法同时考虑数据的流形嵌入映射和稀疏表达,将特征选择问题转化为一个优 化问题,数据的流形嵌入和稀疏表达作为约束项加入目标函数。设计了三个目标函 数,第一个目标函数描述流形学习的局部性原则,第二个目标函数将原始样本点回归 到低维嵌入空间,第三个目标函数对回归系数进行正则化。针对目标函数非凸的问 题,用迭代的方法来解这个约束优化问题,给出了解该优化问题的算法。优选特征用 于参与后续的分类识别任务。在真实的高光谱数据集上的实验表明,新方法能够提高 分类的精度。

A method of hyperspectral image classification based on unsupervised feature sel ection

In the hyperspectral image classification,the rich data enhance its l and-cover recognition ability.However,the problem of "dimension disaster" is caused by t he large number of sample features and the small number of labeled training sample points.In thi s paper,an unsupervised feature selection based hyperspectral image classification method is proposed,which takes into account both the manifold embedded mapping and sparse representation of the data.The feature selection problem is transformed into an optimization problem,in which the manifold embedding and sparse expression of the data are used as constraint items and add ed to the objective function.Three objective functions are designed,the first objective function describes the local princip le of manifold learning,the second objective function returns the original sample point to the low dimensional embedding space,and the third objective function is regularization of the regre ssion coefficient. Aiming at the non-convex problem of the objective function,an iterative method is proposed to solve this constrained optimization problem,and an algorithm for solving the op timization problem is given.Preferred features are used to participate in subsequent class ification recognition tasks.Experiments on real hyperspectral datasets show that the new method can i mprove the accuracy of classification.