Multiple support vector machines for land cover change detection: An application for mapping urban extensions

The reliability of support vector machines for classifying hyper-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection. First, SVM-based change detection is presented and performed for mapping urban growth in the Algerian capital. Different performance indicators, as well as a comparison with artificial neural networks, are used to support our experimental analysis. In a second step, a combination framework is proposed to improve change detection accuracy. Two combination rules, namely, Fuzzy Integral and Attractor Dynamics, are implemented and evaluated with respect to individual SVMs. Recognition rates achieved by individual SVMs, compared to neural networks, confirm their efficiency for land cover change detection. Furthermore, the relevance of SVM combination is highlighted.

[1]  G. Mercier,et al.  Support vector machines for hyperspectral image classification with spectral-based kernels , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[2]  Sung-Bae Cho,et al.  Combining multiple neural networks by fuzzy integral for robust classification , 1995, IEEE Trans. Syst. Man Cybern..

[3]  Xiaolong Dai,et al.  Development of a new automated land cover change detection system from remotely sensed imagery based on artificial neural networks , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[4]  Hassiba Nemmour,et al.  Kalman filtering as a multilayer perceptron training algorithm for detecting changes in remotely sensed imagery , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[5]  Hassiba Nemmour,et al.  Fuzzy neural network architecture for change detection in remotely sensed imagery , 2006 .

[6]  Alan H. Strahler,et al.  Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change pro , 1994 .

[7]  A. Steinhage,et al.  Multiple classifier system based on attractor dynamics , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[8]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[9]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[11]  A. Steinhage,et al.  Attractor dynamics to fuse strongly perturbed sensor data , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[12]  Sung-Bae Cho,et al.  Fuzzy aggregation of modular neural networks with ordered weighted averaging operators , 1995, Int. J. Approx. Reason..

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

[14]  R. Congalton.  Accuracy assessment and validation of remotely sensed and other spatial information , 2001 .

[15]  Robert A. Schowengerdt,et al.  A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery , 1995 .

[16]  H. P. Huang,et al.  Fuzzy Support Vector Machines for Pattern Recognition and Data Mining , 2002 .

[17]  Sung-Bae Cho,et al.  Fusion of neural networks with fuzzy logic and genetic algorithm , 2002, Integr. Comput. Aided Eng..

[18]  J. A. Gualtieri,et al.  Support vector machines for classification of hyperspectral data , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[19]  Mark J. Carlotto.  Detection and analysis of change in remotely sensed imagery with application to wide area surveillance , 1997, IEEE Trans. Image Process..

[20]  Martin Brown,et al.  Linear spectral mixture models and support vector machines for remote sensing , 2000, IEEE Trans. Geosci. Remote. Sens..