Time-series processing of large scale remote sensing data with extreme learning machine

Nowadays, land-cover change detection plays a more and more important role in environment protection and many other fields. However, the current land-cover change detection methods encounter the problems of low accuracy and low efficiency, especially in dealing with large scale remote sensing (RS) data. This paper presents a novel extreme learning machine (ELM) based land-cover change detection method with high testing accuracy and fast processing speed. The evaluation results show that ELM outperforms the traditional methods, e.g., SVM and BP network, in terms of training speed and generalization performance, when applied in land-cover classification. In our experiments, we apply our method to the analysis of rapid land use change in Taihu Lake region over the past decade.

[1]  Robert K. L. Gay,et al.  Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning , 2009, IEEE Transactions on Neural Networks.

[2]  Guang-Bin Huang,et al.  Classification ability of single hidden layer feedforward neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3]  Am Mudabeti,et al.  Remote sensing 1 , 2013 .

[4]  Francesca Bovolo,et al.  Detection of Land-Cover Transitions in Multitemporal Remote Sensing Images With Active-Learning-Based Compound Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Korris Fu-Lai Chung,et al.  Positive and negative fuzzy rule system, extreme learning machine and image classification , 2011, Int. J. Mach. Learn. Cybern..

[6]  P. Saratchandran,et al.  Multicategory Classification Using An Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[7]  David M. Johnson,et al.  Impacts of vegetation dynamics on the identification of land-cover change in a biologically complex community in North Carolina, USA , 2002 .

[8]  Ashish Ghosh,et al.  Unsupervised Change Detection in Remote-Sensing Images Using Modified Self-Organizing Feature Map Neural Network , 2007, 2007 International Conference on Computing: Theory and Applications (ICCTA'07).

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

[10]  Chris Clifton.  Change Detection in Overhead Imagery Using Neural Networks , 2004, Applied Intelligence.

[11]  Mahesh Pal.  Extreme‐learning‐machine‐based land cover classification , 2008, ArXiv.

[12]  Yong Yu,et al.  Sales forecasting using extreme learning machine with applications in fashion retailing , 2008, Decis. Support Syst..

[13]  N. Lam,et al.  Spatial methods for characterising land cover and detecting land-cover changes for the tropics , 2002 .

[14]  Ashish Ghosh,et al.  Unsupervised Change Detection in Remote-Sensing Images using One-dimensional Self-Organizing Feature Map Neural Network , 2006, 9th International Conference on Information Technology (ICIT'06).

[15]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[16]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[17]  Qihao Weng.  Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. , 2002, Journal of environmental management.

[18]  Hanqing Lu,et al.  Unsupervised Change Detection in High Spatial Resolution Optical Imagery Based on Modified Hopfield Neural Network , 2008, 2008 Fourth International Conference on Natural Computation.