Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images.

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Sun, Meijun; Benediktsson, Jon Atli

Abstract

Although extreme learning machine (ELM) has successfully been applied to a number of pattern recognition problems, only with the original ELM it can hardly yield high accuracy for the classification of hyperspectral images (HSIs) due to two main drawbacks. The first is due to the randomly generated initial weights and bias, which cannot guarantee optimal output of ELM. The second is the lack of spatial information in the classifier as the conventional ELM only utilizes spectral information for classification of HSI. To tackle these two problems, a new framework for ELM-based spectral-spatial classification of HSI is proposed, where probabilistic modeling with sparse representation and weighted composite features (WCFs) is employed to derive the optimized output weights and extract spatial features. First, ELM is represented as a concave logarithmic-likelihood function under statistical modeling using the maximum a posteriori estimator. Second, sparse representation is applied to the Laplacian prior to efficiently determine a logarithmic posterior with a unique maximum in order to solve the ill-posed problem of ELM. The variable splitting and the augmented Lagrangian are subsequently used to further reduce the computation complexity of the proposed algorithm. Third, the spatial information is extracted using the WCFs to construct the spectral-spatial classification framework. In addition, the lower bound of the proposed method is derived by a rigorous mathematical proof. Experimental results on three publicly available HSI data sets demonstrate that the proposed methodology outperforms ELM and also a number of state-of-the-art approaches.

Citation

CAO, F., YANG, Z., REN, J., LING, W.-K., ZHAO, H., SUN, M. and BENEDIKTSSON, J.A. 2018. Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 56(11), pages 6263-6279. Available from: https://doi.org/10.1109/tgrs.2018.2828601

• Journal Article Type: Article
• Acceptance Date: Apr 15, 2018
• Online Publication Date: May 9, 2018
• Publication Date: Nov 30, 2018
• Deposit Date: Mar 22, 2022
• Publicly Available Date: Mar 22, 2022
• Journal: IEEE transactions on geoscience and remote sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 1558-0644
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 56
• Issue: 11
• Pages: 6263-6279
• DOI: https://doi.org/10.1109/tgrs.2018.2828601
• Keywords: Extreme learning machine (ELM); Hyperspectral image (HSI); Laplacian prior; Maximum a posteriori (MAP); Sparse representation; Spectral–spatial classification
• Public URL: https://rgu-repository.worktribe.com/output/1085475