Optimized extreme learning machine for urban land cover classification using hyperspectral imagery

This work presents a new urban land cover classification framework using the firefly algorithm (FA) optimized extreme learning machine (ELM). FA is adopted to optimize the regularization coefficient C and Gaussian kernel σ for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.

[1]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[2]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

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

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

[5]  Bo Huang,et al.  Modeling urban vertical growth using cellular automata—Guangzhou as a case study , 2014 .

[6]  Qing He,et al.  Extreme Support Vector Machine Classifier , 2008, PAKDD.

[7]  Qian Du,et al.  Semisupervised Discriminant Analysis for Hyperspectral Imagery With Block-Sparse Graph , 2015, IEEE Geoscience and Remote Sensing Letters.

[8]  Qian Du,et al.  Collaborative-Representation-Based Nearest Neighbor Classifier for Hyperspectral Imagery , 2015, IEEE Geoscience and Remote Sensing Letters.

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

[10]  Hong Sun,et al.  Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Jon Atli Benediktsson,et al.  Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods , 2013, IEEE Signal Processing Magazine.

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

[13]  Hongming Zhou,et al.  Optimization method based extreme learning machine for classification , 2010, Neurocomputing.

[14]  Hui Lin,et al.  An automatic extraction method for individual tree crowns based on self-adaptive mutual information and tile computing , 2015, Int. J. Digit. Earth.

[15]  Min Chen,et al.  A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data , 2015, Remote. Sens..

[16]  Naif Alajlan,et al.  Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images , 2014, IEEE Geoscience and Remote Sensing Letters.

[17]  Maurice Borgeaud,et al.  Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Chen Chen,et al.  Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine , 2014, Remote. Sens..

[19]  Qian Du,et al.  Hyperspectral Band Selection Using Improved Firefly Algorithm , 2016, IEEE Geoscience and Remote Sensing Letters.

[20]  Jun Li,et al.  ${{\rm E}^{2}}{\rm LMs}$ : Ensemble Extreme Learning Machines for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Liangpei Zhang,et al.  On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[22]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[23]  Yong Dou,et al.  Classification of Hyperspectral Remote Sensing Image Using Hierarchical Local-Receptive-Field-Based Extreme Learning Machine , 2016, IEEE Geoscience and Remote Sensing Letters.

[24]  Zheng Zhao.  The Parameters Selection of Genetic Algorithms in Texture Classification , 2000 .

[25]  V. Mani,et al.  Clustering using firefly algorithm: Performance study , 2011, Swarm Evol. Comput..

[26]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[27]  Xin-She Yang,et al.  Firefly Algorithm: Recent Advances and Applications , 2013, ArXiv.

[28]  Zhaohui Xue,et al.  Harmonic Analysis for Hyperspectral Image Classification Integrated With PSO Optimized SVM , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Shiming Xiang,et al.  Semisupervised Hyperspectral Image Classification via Discriminant Analysis and Robust Regression , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Bo Du,et al.  Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding , 2015, Pattern Recognit..

[31]  Chein-I. Chang.  Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .