基于局部敏感判别宽度学习的高光谱图像分类

宽度学习系统(BLS)以其良好的学习性能与泛化能力，在高光谱图像(HSI)分类中得到了广泛应用。然而宽度学习系统仅关注各类样本的可分性，忽略了样本之间的相对关系以及所蕴涵的判别信息，在一定程度上限制了宽度学习系统在高光谱图像分类任务中的性能。为此，提出一种局部敏感判别的宽度学习系统(LSDBLS)方法。该方法通过引入局部敏感判别分析考虑标记样本的判别信息与数据样本的局部流形结构，通过标记样本构建类内图和类间图来表征数据样本之间的相对关系。在此基础上，将类内图和类间图引入到宽度学习系统的目标函数中，通过最小化类内图以及最大化类间图，使得同类样本尽可能地聚集，不同类的样本尽可能地远离，增强LSDBLS对数据特征的判别能力。通过在三个HSI数据集上的实验结果表明，LSDBLS取得了良好的效果。

Locality sensitive discriminative broad learning system for hyperspectral image classification

Recently, BLS has been widely used in HSI classification with its excellent learning performance and generalization ability. However, BLS only focuses on the separability of various samples, ignoring the relative relationship between samples and the discriminative information. To some extent, it limits the performance of BLS. Therefore, this paper proposed a local sensitive discriminative broad learning system(LSDBLS) method. LSDBLS considered the discriminative information of labeled samples and the local manifold structure of data samples by introducing local sensitive discriminant analysis, and constructed intra-class and inter-class graphs by labeled samples to representation the relative relationship between data samples. On this basis, it introduced the intra-class graph and the inter-class graph into the objective function of the broad learning system. By minimizing the intra-class graph and maximizing the inter-class graph, it aggregated the samples of the same class as much as possible, and the samples of different classes were as much as possible, so as to enhance the discriminative ability of LSDBLS for data features. Experimental results on three HSI datasets show that LSDBLS achieves good performance.