中文摘要：

针对多数传统分类算法应用于高光谱分类都存在运算速度慢、精度比较低和难以收敛等问题,从稀疏表示基本理论出发建立了一个基于自适应稀疏表示的高光谱分类模型.利用训练样本构建字典,聚类每一步迭代所产生的余项,将聚类中心作为新的字典原子,然后将测试样本看成冗余字典中训练样本的线性组合,令字典能够更适应于样本的稀疏表示.利用华盛顿地区的HYDICE高光谱遥感数据进行试验,并且与主成分分析、线性鉴别分析、支持向量机、神经网络算法进行比较,结果表明,该算法的总体分类精度比其他算法提高了约12％,有效提高了高光谱影像的分类精度.

英文摘要：

Some traditional algorithms applied in hyperspectral remote sensing image classification have some problems such as low computing rate, low accuracy and hard for convergence. According to sparse rep- resentation theory, a classification model based on adaptive sparse representation （ASP） is constructed. The algorithm collects a few training samples from a structured dictionary, clusters the error vectors of each step, and signs the cluster center as new atoms making the dictionary. Then the testing samples are regar- ded as a linear combination of a few training samples of the structured dictionary so as to make the dictionary more suitable for a spare representation of samples. The ASP model is applied to the hyperspectral image of the Washington captured by an HYDICE sensor, and the experimental results show that it has more advan- tages in the classification in contrast with principal component analysis classifier, linear discriminant analysis classifier, neural network classifier and support vector machine classifier. The overall accuracy of the pro- posed algorithm is improved by 12 % as compared with other methods, which demonstrates the effectiveness of ASP.