基于稀疏表示和学习图正则的高光谱图像特征提取

针对传统局部特征提取算法难以确定邻域参数,以及仅考虑数据间的单一结构而漏掉重要信息的问题,提出一种基于稀疏表示和学习图正则的局部判别与全局稀疏保持投影算法。该算法首先对稀疏表示模型施加基于学习的图正则器,用该改进的稀疏表示模型自适应揭示样本数据间的局部线性结构,通过局部判别模型全局集成算法来提取局部线性结构中的判别信息;利用基于学习图正则稀疏表示模型构建的新型稀疏图来揭示数据间的全局稀疏结构;使得数据的局部判别结构和全局稀疏结构在低维特征空间得以保持。通过1-近邻和支持向量机分类器对实验结果进行评估,在PaviaU和Indian Pines两个高光谱公共数据集上的实验显示,提出的局部判别与全局稀疏保持投影算法较对比算法取得了最好的性能,由于提取了全局和局部的判别信息,有效提升了高光谱图像的地物分类精度。

Feature Extraction of Hyperspectral Image Based on Sparse Representation and Learning Graph Regularity

Traditional local feature extraction algorithms are difficult to determine neighborhood parameters,and they only consider the single structure information of the data,which ignore important information.To solve the above problems,a Local Discrimination and Global Sparse Preservation Projection Algorithm(LDGSPP)based on sparse representation and learning graph regularity is proposed.The algorithm firstly applies a learning-based graph regularizer to the sparse representation model.Then the improved sparse representation model is used to reveal the local linear structure of the sample data adaptively.The local discriminant model global integration algorithm is used to extract the discriminant information of the local linear structure.The new sparse graph constructed by the improved sparse representation model is used to reveal the global sparse structure of data.The local discriminant structure and the global sparse structure of the data are preserved in the low dimensional feature space.1-nearest neighbors and support vector machine classifier are used to evaluate the experimental results.The experiments on PaviaU and Indian Pines show that LDGSPP achieves the best performance compared with the comparison algorithm.As global and local discriminant information is extracted,the ground object classification accuracy of hyperspectral images is effectively improved.