References
- Azari, M., A. Tayyebi, M. Helbich, and M. A. Reveshty. 2016. “Integrating Cellular Automata, Artificial Neural Network and Fuzzy Set Theory to Simulate Threatened Orchards: Application to Maragheh, Iran.” GIScience & Remote Sensing 53 (2): 183–205. doi:10.1080/15481603.2015.1137111.
- Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Singapore: Springer.
- Bruzzone, L., and M. Marconcini. 2009. “Toward the Automatic Updating of Land-Cover Maps by a Domain-Adaptation SVM Classifier and a Circular Validation Strategy.” IEEE Transactions on Geoscience and Remote Sensing 47 (4): 1108–1122. doi:10.1109/Tgrs.2008.2007741.
- Chang, N. B., M. Han, W. Yao, L. C. Chen, and S. Xu. 2010. “Change Detection of Land Use and Land Cover in an Urban Region with SPOT-5 Images and Partial Lanczos Extreme Learning Machine.” Journal of Applied Remote Sensing 4 (1): 043551-043551-15. doi:10.1117/1.3518096.
- Cheng, G., L. Guo, T. Zhao, J. Han, H. Li, and J. Fang. 2013. “Automatic Landslide Detection from Remote-sensing Imagery Using a Scene Classification Method Based on BoVW and pLSA.” International Journal of Remote Sensing 34 (1): 45–59. doi:10.1080/01431161.2012.705443.
- Du, Y., D. Wu, F. Liang, and C. Li. 2013. “Integration of Case-based Reasoning and Object-based Image Classification to Classify SPOT Images: A Case Study of Aquaculture Land Use Mapping in Coastal Areas of Guangdong Province, China.” GIScience & Remote Sensing 50 (5): 574–589. doi:10.1080/15481603.2013.842292.
- Giri, C., B. Pengra, J. Long, and T. R. Loveland. 2013. “Next Generation of Global Land Cover Characterization, Mapping, and Monitoring.” International Journal of Applied Earth Observation and Geoinformation 25: 30–37. doi:10.1016/j.jag.2013.03.005.
- Grossberg, S. 2013. “Adaptive Resonance Theory: How a Brain Learns to Consciously Attend, Learn, and Recognize a Changing World.” Neural Networks 37: 1–47. doi:10.1016/j.neunet.2012.09.017.
- Huang, X., Q. Lu, and L. Zhang. 2014. “A Multi-index Learning Approach for Classification of High-resolution Remotely Sensed Images Over Urban Areas.” ISPRS Journal of Photogrammetry and Remote Sensing 90: 36–48. doi:10.1016/j.isprsjprs.2014.01.008.
- Hussain, E., and J. Shan. 2015. “Object-based Urban Land Cover Classification Using Rule Inheritance Over Very High-resolution Multisensor and Multitemporal Data.” GIScience & Remote Sensing 53 (2): 1–19. doi:10.1080/15481603.2015.1122923.
- Inglada, J. 2003. “Change Detection on SAR Images by Using a Parametric Estimation of the Kullback-Leibler Divergence.” IEEE Proceedings of International Geoscience and Remote Sensing Symposium 6: 4104–4106. doi:10.1109/IGARSS.2003.1295376.
- Kullback, S., and R. A. Leibler. 1951. “On Information and Sufficiency.” The Annals of Mathematical Statistics 22 (1): 79–86. doi:10.1214/aoms/1177729694.
- Li, J., J. M. Bioucas-Dias, and A. Plaza. 2013. “Spectral–spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning.” IEEE Transactions on Geoscience and Remote Sensing 51 (2): 844–856. doi:10.1109/TGRS.2012.2205263.
- Li, M., J. Im, and C. Beier. 2013. “Machine Learning Approaches for Forest Classification and Change Analysis Using Multi-temporal Landsat TM Images Over Huntington Wildlife Forest.” GIScience & Remote Sensing 50 (4): 361–384. doi:10.1080/15481603.2013.819161.
- Liu, D., M. Kelly, and P. Gong. 2006. “A Spatial–temporal Approach to Monitoring Forest Disease Spread Using Multi-temporal High Spatial Resolution Imagery.” Remote Sensing of Environment 101 (2): 167–180. doi:10.1016/j.rse.2005.12.012.
- Liu, Y., and X. Li. 2014. “Domain Adaptation for Land Use Classification: A Spatio-temporal Knowledge Reusing Method.” ISPRS Journal of Photogrammetry and Remote Sensing 98: 133–144. doi:10.16/j.isprsjprs.2014.09.013.
- Mishra, D. R., S. Narumalani, D. Rundquist, and M. Lawson. 2005. “Characterizing the Vertical Diffuse Attenuation Coefficient for Downwelling Irradiance in Coastal Waters: Implications for Water Penetration by High Resolution Satellite Data.” ISPRS Journal of Photogrammetry and Remote Sensing 60 (1): 48–64. doi:10.1016/j.isprsjprs.2005.09.003.
- Otukei, J. R., and T. Blaschke. 2010. “Land Cover Change Assessment Using Decision Trees, Support Vector Machines and Maximum Likelihood Classification Algorithms.” International Journal of Applied Earth Observation and Geoinformation 12: S27–S31. doi:10.1016/j.jag.2009.11.002.
- Pal, M., A. E. Maxwell, and T. A. Warner. 2013. “Kernel-based Extreme Learning Machine for Remote-sensing Image Classification.” Remote Sensing Letters 4 (9): 853–862. doi:10.1080/2150704X.2013.805279.
- Phillips, R. D., L. T. Watson, D. R. Easterling, and R. H. Wynne. 2014. “An SMP Soft Classification Algorithm for Remote Sensing.” Computers & Geosciences 68: 73–80. doi:10.1016/j.cageo.2014.03.010.
- Salmon, J. M., M. A. Friedl, S. Frolking, D. Wisser, and E. M. Douglas. 2015. “Global Rain-fed, Irrigated, and Paddy Croplands: A New High Resolution Map Derived from Remote Sensing, Crop Inventories and Climate Data.” International Journal of Applied Earth Observation and Geoinformation 38: 321–334. doi:10.1016/j.jag.2015.01.014.
- Seto, K. C., and W. Liu. 2003. “Comparing ARTMAP Neural Network with the Maximum-likelihood Classifier for Detecting Urban Change.” Photogrammetric Engineering & Remote Sensing 69 (9): 981–990. doi:10.14358/PERS.69.9.981.
- Su, Y., X. Chen, C. Wang, H. Zhang, J. Liao, Y. Ye, and C. Wang. 2015. “A New Method for Extracting Built-up Urban Areas Using DMSP-OLS Nighttime Stable Lights: A Case Study in the Pearl River Delta, Southern China.” GIScience & Remote Sensing 52 (2): 218–238. doi:10.1080/15481603.2015.1007778.
- Tuia, D., E. Pasolli, and W. J. Emery. 2011. “Using Active Learning to Adapt Remote Sensing Image Classifiers.” Remote Sensing of Environment 115 (9): 2232–2242. doi:10.1016/j.rse.2011.04.022.
- Vigdor, B., and B. Lerner. 2007. “The Bayesian ARTMAP.” IEEE Transactions on Neural Networks 18 (6): 1628–1644. doi:10.1109/Tnn.2007.900234.
- Wagner, W., A. Ullrich, V. Ducic, T. Melzer, and N. Studnicka. 2006. “Gaussian Decomposition and Calibration of a Novel Small-footprint Full-waveform Digitising Airborne Laser Scanner.” ISPRS Journal of Photogrammetry and Remote Sensing 60 (2): 100–112. doi:10.1016/j.isprsjprs.2005.12.001.
- Xu, J., R. Hang, and Q. Liu. 2014. “Patch-based Active Learning (PTAL) for Spectral-spatial Classification on Hyperspectral Data.” International Journal of Remote Sensing 35 (5): 1846–1875. doi:10.1080/01431161.2013.879349.
- Zhao, K., D. Valle, S. Popescu, X. Zhang, and B. Mallick. 2013. “Hyperspectral Remote Sensing of Plant Biochemistry Using Bayesian Model Averaging with Variable and Band Selection.” Remote Sensing of Environment 132: 102–119. doi:10.1016/j.rse.2012.12.026.