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Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification

Remote sensing (Basel, Switzerland), 2019, Vol.11 (17), p.1959 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11171959

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  • Title:
    Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification
  • Author: Xue, Zhaohui ; Yang, Sirui ; Zhang, Hongyan ; Du, Peijun
  • Subjects: Algorithms ; attribute profiles ; Classification ; coupled tensor factorization ; data fusion ; Data integration ; Decomposition ; Discriminant analysis ; Factorization ; Feature extraction ; hyperspectral remote sensing image (HSI) ; Laboratories ; Lidar ; light detection and ranging (LiDAR) ; Mathematical analysis ; Mathematical morphology ; Matrix methods ; Methods ; Morphology ; Multisensor fusion ; Remote sensing ; Tensors
  • Is Part Of: Remote sensing (Basel, Switzerland), 2019, Vol.11 (17), p.1959
  • Description: Hyperspectral and light detection and ranging (LiDAR) data fusion and classification has been an active research topic, and intensive studies have been made based on mathematical morphology. However, matrix-based concatenation of morphological features may not be so distinctive, compact, and optimal for classification. In this work, we propose a novel Coupled Higher-Order Tensor Factorization (CHOTF) model for hyperspectral and LiDAR data classification. The innovative contributions of our work are that we model different features as multiple third-order tensors, and we formulate a CHOTF model to jointly factorize those tensors. Firstly, third-order tensors are built based on spectral-spatial features extracted via attribute profiles (APs). Secondly, the CHOTF model is defined to jointly factorize the multiple higher-order tensors. Then, the latent features are generated by mode-n tensor-matrix product based on the shared and unshared factors. Lastly, classification is conducted by using sparse multinomial logistic regression (SMLR). Experimental results, conducted with two popular hyperspectral and LiDAR data sets collected over the University of Houston and the city of Trento, respectively, indicate that the proposed framework outperforms the other methods, i.e., different dimensionality-reduction-based methods, independent third-order tensor factorization based methods, and some recently proposed hyperspectral and LiDAR data fusion and classification methods.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2072-4292
    EISSN: 2072-4292
    DOI: 10.3390/rs11171959
  • Source: ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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