Hyperspectral image classification by AdaBoost weighted composite kernel extreme learning machines

Abstract Extreme learning machine (ELM) is an efficient learning algorithm for multi-classification and regression. However, original ELM doesn't consider the weight of each sample in training-set, which may cause the accuracy decreasing especially in imbalanced datasets. Even if each training sample is assigned with an extra weight, the problem on how to determinate the weight adaptively still remains. Inspiration by AdaBoost algorithm, we embed the weighted ELM algorithm in AdaBoost framework. In the meanwhile, we incorporate spatial and spectral information in composite kernel for each sample, which has a good performance in hyperspectral image (HSI) classification. By combining composite kernel methods and Adaboost framework with weighted ELM, a novel algorithm, namely AdaBoost composite kernel extreme learning machines denoted as AdaBoost-WCKELM is proposed. Experimental results demonstrate that the proposed method outperforms current state-of-the-art algorithms and derives a good improvement in HSI classification accuracy.

[1]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Yiqiang Chen,et al.  Weighted extreme learning machine for imbalance learning , 2013, Neurocomputing.

[3]  Jon Atli Benediktsson,et al.  Advances in Spectral-Spatial Classification of Hyperspectral Images , 2013, Proceedings of the IEEE.

[4]  Timothy A. Warner,et al.  Kernel-based extreme learning machine for remote-sensing image classification , 2013 .

[5]  Patrick Hébert,et al.  Median Filtering in Constant Time , 2007, IEEE Transactions on Image Processing.

[6]  Jon Atli Benediktsson,et al.  Generalized Composite Kernel Framework for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Gustavo Camps-Valls,et al.  Composite kernels for hyperspectral image classification , 2006, IEEE Geoscience and Remote Sensing Letters.

[8]  Erchan Aptoula.  Hyperspectral Image Classification With Multidimensional Attribute Profiles , 2015, IEEE Geoscience and Remote Sensing Letters.

[9]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[10]  Liang Xiao,et al.  Hyperspectral image classification via region-based composite kernels , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[11]  Xiangtao Zheng,et al.  Dimensionality Reduction by Spatial–Spectral Preservation in Selected Bands , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Yong Liu,et al.  Multi-class AdaBoost ELM , 2015 .

[13]  Jianping Yin,et al.  Boosting weighted ELM for imbalanced learning , 2014, Neurocomputing.

[14]  Xiangtao Zheng,et al.  Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection , 2017, IEEE Transactions on Image Processing.

[15]  Bor-Chen Kuo,et al.  An automatic method to determine the coefficient of the composite kernel for hyperspectral image classification , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[16]  Xiangtao Zheng,et al.  Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Jason Weston,et al.  Semisupervised Neural Networks for Efficient Hyperspectral Image Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[19]  Bor-Chen Kuo,et al.  Kernel-Based KNN and Gaussian Classifiers for Hyperspectral Image Classification , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[20]  D. Böhning.  Multinomial logistic regression algorithm , 1992 .

[21]  Karim Saheb Ettabaa,et al.  Spectral-spatial classification of hyperspectral images using different spatial features and composite kernels , 2014, International Image Processing, Applications and Systems Conference.

[22]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Antonio J. Plaza,et al.  Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[25]  Yicong Zhou,et al.  Extreme Learning Machine With Composite Kernels for Hyperspectral Image Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.