Land Cover Classification from MODIS Satellite Data Using Probabilistically Optimal Ensemble of Artificial Neural Networks
Terra and Aqua, 2 satellites launched by the NASA-centered international Earth Observing System project, house MODIS (Moderate Resolution Imaging Spectroradiometer) sensors. Moderate resolution remote sensing allows the quantifying of land surface type and extent, which can be used to monitor changes in land cover and land use for extended periods of time. In this paper, we propose applying a probabilistically optimal ensemble technique, based on fault masking among individual classifier for N-version programming. We create an optimal ensemble of artificial neural networks and use the majority voting result to predict land surface cover from MODIS data. We show that an optimal ensemble of neural networks greatly improves the classification error rate of land cover type.
KeywordsLand Cover Land Cover Type Land Cover Classification Moderate Resolution Image Spectroradiometer Classification Error Rate
Unable to display preview. Download preview PDF.
- 1.Shkvarko, Y., Montiel, J., Rizo, L., Salas, J.: Neural Network-Based Signal Processing for Enhancing the Multi-Sensor Remote Sensing Imagery. In: 14th International Conference on Electronics, Communications and Computers, pp. 168–172 (2004)Google Scholar
- 2.Roli, F., Serpico, S.B., Bruzzone, L.: Classification of Multisensor Remote-Sensing Images by Multiple Structured Neural Networks. In: 13th International Conference on Pattern Recognition (ICPR 1996), vol. 4, pp. 180–184 (1996)Google Scholar
- 3.Kushardono, D., Fukue, K., Shimoda, H., Sakata, T.: A Study on Neural Network Landcover Classification Models with the aid of Co-occurence Matrix for Multiband Images. Journal of The Remote Sensing Society of Japan 16(1), 36–49 (1996) (in Japanese)Google Scholar
- 4.Imamura, K., Smith, K.: A Probabilistically Optimal Ensemble Technique for Training Based Classifiers. In: Proceedings of Joint 2nd International Conference on Soft Computing and Intelligent Systems and 5th International Symposium on Advanced Intelligent Systems, Japan (2004)Google Scholar