Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development
Reference33 articles.
1. Agarwal, R., Ranjan, P., & Chipman, H. (2013). A new Bayesian ensemble of trees classifier for identifying multi-class labels in satellite images. Canadian Journal of Remote Sensing, 39(6), 507–520.
2. Atkinson, P. M., & Naser, D. K. (2010). A geostatistical weighted k-NN classifier for remotely sensed Imagery. Geographical Analysis, 42(2), 204–225.
3. Bazi, Y., & Melgani, F. (2006). Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 44(11), 3374–3385.
4. Blinn, C. E. (2005). Increasing the precision of forest area estimates through improved sampling for nearest neighbor satellite image classification. Ph.D Dissertation: Virginia Polytechnic Institute and State University.
5. Bottou, L., Cortes, C., Denker, J., Drucker, H., Guyon, I., Jackel, L., LeCun, Y., et al. (1994). Comparison of classifiers methods—A case study in handwriting digit recognition. In Proceedings of international conference on pattern recognition, 77–87.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献