论文标题
基于模糊性的空间 - 光谱级别判别信息保存高光谱图像分类的主动学习
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification
论文作者
论文摘要
高光谱图像分类（HSIC）的传统活动/自我/互动学习会增加训练集的大小，而无需考虑现有样本和新样本之间的类别散射和随机性。其次，对联合频谱空间信息进行了非常有限的研究，最后，一个次要但值得一提的是停止标准，社区并没有太多考虑。因此，这项工作提出了一种基于模糊性的新型空间光谱，用于本地和全球类别的判别信息（FLG）方法。我们首先研究了基于空间模糊性的错误分类样本信息。然后，我们计算在类信息内部和类信息之间的本地和全局，并以细粒度的方式进行表达。后来，此信息被馈送到一个判别目标函数中，以查询异质样品，从而消除训练样本之间的随机性。基准HSI数据集的实验结果证明了FLG方法对生成，极端学习机和稀疏多项式逻辑回归（SMLR） - 隆语分类器的有效性。
Traditional Active/Self/Interactive Learning for Hyperspectral Image Classification (HSIC) increases the size of the training set without considering the class scatters and randomness among the existing and new samples. Second, very limited research has been carried out on joint spectral-spatial information and finally, a minor but still worth mentioning is the stopping criteria which not being much considered by the community. Therefore, this work proposes a novel fuzziness-based spatial-spectral within and between for both local and global class discriminant information preserving (FLG) method. We first investigate a spatial prior fuzziness-based misclassified sample information. We then compute the total local and global for both within and between class information and formulate it in a fine-grained manner. Later this information is fed to a discriminative objective function to query the heterogeneous samples which eliminate the randomness among the training samples. Experimental results on benchmark HSI datasets demonstrate the effectiveness of the FLG method on Generative, Extreme Learning Machine and Sparse Multinomial Logistic Regression (SMLR)-LORSAL classifiers.
