Multiorder Interaction Information Embedding-Based Multiview Fusion-Aided Hyperspectral Image Classification

作     者：Li, Xuefei Cao, Weijia Zhang, Kai Liu, Baodi Tao, Dapeng Liu, Weifeng 

作者机构：China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100101 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Yangtze Three Gorges Technol & Econ Dev Co Ltd Beijing 100038 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Yunnan Peoples R China 

出 版 物：《IEEE GEOSCIENCE AND REMOTE SENSING LETTERS》 (IEEE Geosci. Remote Sens. Lett.)

年 卷 期：2022年第19卷

页      面：1页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：Open Project Program of the National Laboratory of Pattern Recognition (NLPR) Major Scientific and Technological Projects of China National Petroleum Corporation (CNPC) [ZD2019-183-008] National Natural Science Foundation of China 

主　　题：Feature extraction Correlation Petroleum Principal component analysis Optimization Geoscience and remote sensing Task analysis Hyperspectral image (HSI) classification interaction information loss fusion multiview learning (MVL) 

摘      要：Hyperspectral images (HSIs) are obtained from hyperspectral imaging sensors, which capture information in hundreds of spectral bands of objects. However, how to take full advantage of spatial and spectral information from many spectral bands to improve the performance of HSI classification remains an open question. Many HSI classification works have recently been reported by employing multiview learning (MVL) algorithms that can fully use complementary information between different view features and thus have received widespread attention. This letter proposes a multiview fusion network based on multiorder interaction information embedding for HSI classification. First, the correlation matrix between spectral bands is used to divide the original data into multiple subsets as local views. The subset after the segmented-PCA process is used as the global view. Second, the features of different views are extracted separately using a feature extraction network and mapped to the same dimension. Prefusion is achieved by multiorder interaction of various view features. Finally, loss-weighted fusion is applied to each view according to its contribution to the classification task. To evaluate the effectiveness of the proposed method, complete experiments were conducted on three commonly used HSI datasets, namely Pavia University, Houston 2013, and Houston 2018. The experimental results demonstrate that the proposed method improves the classification performance of existing feature extraction networks and is more competitive with other methods in the field.