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

  • role： First author第一作者
  • Affiliation：

    Basic Geographic Information Center of Jiangsu Province, Nanjing 210013, China

  • Email：yuyaoyao_yy@163.com
  • Introduction：虞瑶，研究方向为高光谱遥感、资源环境遥感。E-mail： yuyaoyao_yy@163.com
YU Yao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth Science and Engineering, Hohai University, Nanjing 211100, China

  • Email：hjsu@hhu.edu.cn
  • Introduction：苏红军，研究方向为高光谱遥感、资源环境遥感。E-mail： hjsu@hhu.edu.cn
SU Hongjun2*,  
  • Affiliation：

    Basic Geographic Information Center of Jiangsu Province, Nanjing 210013, China

TAO Yang1

resumen

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.

palabra clave

hyperspectral remote sensing;dynamic selection;shape-adaptive neighborhood;collaborative representation;image classification

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