Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computers in Earth Sciences,Computer Science Applications,Geography, Planning and Development
Reference48 articles.
1. Shougeng, H., & Le, W. (2012). Automated urban land-use classification with remote sensing. International Journal of Remote Sensing, 34(3), 790–803.
2. Qixia, M., Pinliang, D., & Huadong, G. (2015). Pixel- and feature-level fusion of hyperspectral and LIDAR data for urban land-use classification. International Journal of Remote Sensing, 36(6), 1618–1644.
3. Kumar, P., Prasad, R., Choudhary, A., Mishra, V. N., Gupta, D. K., & Srivastava, P. K. (2016). A statistical significance of differences in classification accuracy of crop types using different classification algorithms. Geocarto International, 32(2), 206–224.
4. Kumar, P., Gupta, D. K., Mishra, V. N., & Prasad, R. (2015). Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data. International Journal of Remote Sensing, 36(6), 1604–1617.
5. Shukla, G., Garg, R. D., Srivastava, H. S., & Garg, P. K. (2018). An effective implementation and assessment of a random forest classifier as a soil spatial predictive model. International Journal of Remote Sensing, 39(8), 2637–2669.
Cited by
5 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