结合空间—光谱信息的快速自训练高光谱遥感影像分类  

Fast self-training based on spatial-spectral information for hyperspectral image classification

作　　者：金垚 董燕妮 杜博[5] JIN Yao;DONG Yanni;DU Bo(Institute of Geophysics and Geomatics,China University of Geosciences,Wuhan 430074,China;Hubei Luojia Laboratory,Wuhan 430079,China;State Key Caboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;School of Resource and Environmental Science,Wuhan University,Wuhan 430079,China;School of Computer Science,Wuhan University,Wuhan 430072,China)

机构地区：[1]中国地质大学(武汉)地球物理与空间信息学院,武汉430074 [2]湖北珞珈实验室,武汉430079 [3]武汉大学测绘遥感信息工程国家重点实验室,武汉430079 [4]武汉大学资源与环境科学学院,武汉430079 [5]武汉大学计算机学院,武汉430072

出　　处：《遥感学报》2024年第1期219-230,共12页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:62222116,62171417,41871243,62141112);湖北珞珈实验室开放基金(编号:220100058)。

摘　　要：自训练方法被广泛应用于高光谱影像分类任务中以解决标记样本获取困难的问题。传统的自训练方法不仅忽略了高光谱影像所能提供的空间信息,导致最终分类精度受到影响;同时在每次迭代过程中都需要完成一次对未标记数据的分类任务,导致需要大量的时间成本。因此,针对上述问题,本文提出了一种基于空间—光谱信息的快速自训练方法用于高光谱影像分类。与传统的自训练方法不同,该方法在迭代过程中使用空间—光谱信息对未标记数据进行筛选完成标记样本的扩充,而不是使用分类器对未标记样本进行分类。首先针对初始标记样本使用空间邻域块选择空间近邻点,然后使用自适应阈值对空间近邻点进行二次筛选得到空谱近邻点赋予标记,最后根据扩充后的标记样本对分类器进行训练完成分类任务。结果表明,在Washington DC Mall Subimage高光谱数据集中每类分别选择2个和10个训练样本时,整体分类精度分别达到了93.17%和95.43%;而在Indian Pines数据集中整体分类精度分别达到了59.75%和86.13%。我们提出的结合空间—光谱信息的快速自训练方法和对比方法相比,我们的方法有明显的提升。Hyperspectral image classification has been a popular issue in the field of hyperspectral image interpretation.The prominent problem in hyperspectral image classification currently involves the considerably time-consuming and expensive manual acquisition of labeled samples for hyperspectral images in practical applications.This problem leads to a sparse number of training samples and increases the difficulty of obtaining good classification results.Self-training methods are widely used in hyperspectral image classification to solve the difficulty in labeled sample acquisition in hyperspectral image classification.Traditional self-training methods mostly use spectral information to classify unlabeled samples and then utilize the expanded labeled data set to iteratively train the classifier to complete the classification task.In this model,the spatial information provided by the hyperspectral images is ignored,resulting in poor classification accuracy.Simultaneously,the classification of unlabeled data must be completed once during each iteration,resulting in a significant time cost.Therefore,a fast self-training method based on spatial–spectral information is proposed in this paper for hyperspectral image classification to address the above problems.FST-SS(Fast Self-training based on Spatial-Spectral information)supplements the spatial information in hyperspectral images by exploiting the consistency of the spatial distribution of features in the hyperspectral images.Instead of using the classifier to classify unlabeled samples,this approach uses the spatial–spectral information to filter unlabeled data and extend the labeled samples during the iterative process.The spatial nearest neighbors are first selected using a spatial domain patch for the initial labeled sample.The spatial nearest neighbors are then filtered using an adaptive threshold to obtain the spatial–spectral nearest neighbors to be labeled.Finally,the classifier is trained to complete the classification task based on the expanded labeled samp

关 键 词：高光谱遥感 半监督分类 小样本问题 空间—光谱信息 自训练方法 

分 类 号：P237[天文地球—摄影测量与遥感] P2[天文地球—测绘科学与技术]