联合空间信息的高光谱遥感协同表示动态集成分类算法

Dynamic selection algorithm for collaborative representation of hyperspectral remote sensing based on joint spatial information

近年来,集成学习成为高光谱遥感影像分类的研究热点,尤其是动态集成算法根据测试样本的特征自适应地选择最佳分类器,其分类性能显著提升。然而现有的动态集成方法仅考虑测试样本与验证样本的光谱信息,忽略了高度规则化的高光谱遥感影像包含的丰富空间信息。为进一步提升高光谱遥感影像动态集成算法分类的准确性和可靠性,提出了联合空间信息的可变K邻域动态集成算法VKS(Variable K-neighborhood and Spatial Information)和联合自适应邻域空间信息的可变K邻域动态集成算法VKSA(Variable K-neighborhood with Shape-Adaptive)。两种算法第一阶段综合考虑分类器精度与相似度自适应地改变测试样本的K邻域,第二阶段分别设计固定窗口和自适应窗口的嵌入方式增加地物的局部空间近邻关系,充分利用高光谱遥感影像地物复杂的空间形态结构信息。实验部分采用3组通用的高光谱遥感影像数据对所提出算法的性能进行综合评价。结果表明相比于传统的动态集成算法,本文提出的联合空间信息的动态集成模型能显著提升分类精度,其中基于自适应窗口方式的VKSA算法明显优于基于固定窗口的VKS算法。

Ensemble learning has recently attracted considerable attention for hyperspectral image analysis.This model integrates multiple base classifiers for joint decision making,which is better than using a base classifier.Ensemble learning includes static and dynamic classifier ensembles.In the static ensemble method,the same classifier combination scheme is selected for testing sample.However,this method ignores the difference in classifier performance for each testing sample.Considering the features of testing sample,the best classifier is selected adaptively in dynamic ensemble methods.Therefore,this classifier can generally achieve better performance than static ensemble methods for hyperspectral image classification.However,numerous dynamic ensemble methods only consider the spectral information of the validation and training samples,ignoring the rich spatial information of hyperspectral images.A Variable K-neighborhood and Spatial information algorithm(VKS)is proposed in this paper to further improve the accuracy and reliability of hyperspectral image classification.Firstly,the VKS algorithm comprehensively considers the accuracy and similarity of the classifier to adaptively adjust the K-neighborhood of the testing sample,increasing the reliability and flexibility of the region of competence setting.Thus,the testing samples with good spectral discrimination performance are preferentially classified.The label information of spatial neighborhood samples is used for predicting the testing samples with poor spectral discrimination performance.A fixed window is designed to provide local spatial information in hyperspectral images.However,fixed windows cannot reveal the complex and changeable morphological characteristics of ground objects.An adaptive window that can effectively reflect complex spatial information is proposed to capture the complex and changeable spatial structure in a hyperspectral image,and a variable K-neighborhood with a shape-adaptive(VKSA)algorithm is further designed.The Purdue Campus,Indian Pines,and Salinas hyperspectral remote sensing data sets are used to design experiments and verify the performance of the proposed VKS and VKSA algorithms.Four state-of-the-art methods,namely,majority voting,overall local accuracy,modified local accuracy,and multiple classifier behavior,are used to quantify the classification accuracy.Experimental results demonstrate that the VKS and VKSA algorithms outperform static ensemble methods and three classic dynamic ensemble methods in overall classification accuracy.Moreover,the VKSA algorithm with an adaptive window perform better than the VKS algorithm with a fixed window.