References
- Abadi, M., P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, et al. 2016. “TensorFlow: A System for Large-Scale Machine Learning.” In Symposium on Operating Systems Design and Implementation, 919–936. Savannah, GA, USA. https://tensorflow.org
- Addae, B., and N. Oppelt. 2019. “Land-Use/Land-Cover Change Analysis and Urban Growth Modelling in the Greater Accra Metropolitan Area (GAMA), Ghana.” Urban Science 3 (1): 26. doi:https://doi.org/10.3390/urbansci3010026.
- Alifu, H., Y. Hirabayashi, B. Johnson, J.-F. Vuillaume, A. Kondoh, and M. Urai. 2018. “Inventory of Glaciers in the Shaksgam Valley of the Chinese Karakoram Mountains, 1970–2014.” Remote Sensing 10 (8): 1166. doi:https://doi.org/10.3390/rs10081166.
- Arel, I., D. C. Rose, and T. P. Karnowski. 2010. “Deep Machine Learning – A New Frontier in Artificial Intelligence Research [Research Frontier].” IEEE Computational Intelligence Magazine 5 (4): 13–18. doi:https://doi.org/10.1109/MCI.2010.938364.
- Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:https://doi.org/10.1023/A:1010933404324.
- Chand, P., M. C. Sharma, R. Bhambri, C. V. Sangewar, and N. Juyal. 2017. “Reconstructing the Pattern of the Bara Shigri Glacier Fluctuation since the End of the Little Ice Age, Chandra Valley, North-Western Himalaya.” Progress in Physical Geography 41 (5): 643–675. doi:https://doi.org/10.1177/0309133317728017.
- Congalton, R. G., and K. Green. 2008. “Assessing the Accuracy of Remotely Sensed Data. CRC Press, Taylor & Francis Group.” CRC Press. doi:https://doi.org/10.1201/9781420055139.
- Dams, J., J. Dujardin, R. Reggers, I. Bashir, F. Canters, and O. Batelaan. 2013. “Mapping Impervious Surface Change from Remote Sensing for Hydrological Modeling.” Journal of Hydrology 485: 84–95. doi:https://doi.org/10.1016/j.jhydrol.2012.09.045.
- Dashora, A., B. Lohani, and J. Malik. 2007. “A Repository of Earth Resource Information CORONA Satellite Programme A System for Accurate Estimation of Solar Insolation at A Point and over an Area View Project Visualization of LiDAR Datasets View Project.” Current Science 92 (7): 926–932. https://www.researchgate.net/publication/228656758
- Development Team, Q. G. I. S. 2019. “QGIS Geographic Information System.” Open Source Geospatial Foundation Project. http://qgis.osgeo.org
- Douglas-Mankin, K. R., R. Srinivasan, and J. G. Arnold. 2013. “Soil and Water Assessment Tool (SWAT) Model: Current Developments and Applications.” Transactions of the ASABE 53 (5): 1423–1431. doi:https://doi.org/10.13031/2013.34915.
- Farabet, C., C. Couprie, L. Najman, and Y. Lecun. 2013. “Learning Hierarchical Features for Scene Labeling.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8): 1915–1929. doi:https://doi.org/10.1109/TPAMI.2012.231.
- Fowler, M. J. F. 2004. “Declassified CORONA KH-4B Satellite Photography of Remains from Rome’s Desert Frontier.” International Journal of Remote Sensing 25 (18): 3549–3554. doi:https://doi.org/10.1080/0143116031000098887.
- Galiatsatos, N., D. N. M. Donoghue, and G. Philip. 2008. “High Resolution Elevation Data Derived from Stereoscopic CORONA Imagery with Minimal Ground Control.” Photogrammetric Engineering and Remote Sensing 74 (9): 1093–1106. doi:https://doi.org/10.14358/PERS.74.9.1093.
- Girshick, R., J. Donahue, T. Darrell, and J. Malik. 2014. “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.” In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 580–587. Columbus, OH: IEEE. doi:https://doi.org/10.1109/CVPR.2014.81.
- Gordon, S. I. 1980. “Utilizing LANDSAT Imagery to Monitor Land-Use Change: A Case Study in Ohio.” Remote Sensing of Environment 9 (3): 189–196. doi:https://doi.org/10.1016/0034-4257(80)90028-0.
- Gupta, S., S. Karumanchi, S. Dash, S. Adla, S. Tripathi, R. Sinha, D. Paul, and I. Sen. 2019. “Monitoring Ecosystem Health in India’s Food Basket.” Eos 100 (March). doi:https://doi.org/10.1029/2019EO117683.
- Hamandawana, H., F. Eckardt, and S. Ringrose. 2007. “Proposed Methodology for Georeferencing and Mosaicking Corona Photographs.” International Journal of Remote Sensing 28 (1): 5–22. doi:https://doi.org/10.1080/01431160500104400.
- Haralick, R. M., D. Its’hak, and K. Shanmugam. 1973. “Textural Features for Image Classification.” IEEE Transactions on Systems, Man, and Cybernetics SMC-3 (6): 610–621. doi:https://doi.org/10.1109/TSMC.1973.4309314.
- Hinton, G. E., N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. 2012. “Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors.” arXiv preprint arXiv:1207.0580
- Hwang, J.-T., K.-T. Chang, and H.-C. Chiang. 2011. “Satellite Image Classification Based on Gabor Texture Features and SVM.” In 2011 19th International Conference on Geoinformatics, 1–6. IEEE, Shanghai, China. doi:https://doi.org/10.1109/GeoInformatics.2011.5980774.
- Kingma, D. P., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.” arXiv preprint arXiv:1412.6980
- Kumar, B. 2016. “Spectral-Spatial Classification of Hyperspectral Imagery.” Indian Institute of Technology Kanpur. http://172.28.64.70:8080/jspui/bitstream/123456789/16580/2/12103016.pdf
- Kumar, B., and O. Dikshit. 2014. “Texture Based Hyperspectral Image Classification.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences – ISPRS Archives 40 (8): 793–798. doi:https://doi.org/10.5194/isprsarchives-XL-8-793-2014.
- Kumar, B., and O. Dikshit. 2015. “Spectral-Spatial Classification of Hyperspectral Imagery Based on Moment Invariants.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (6): 2457–2463. IEEE. doihttps://doi.org/10.1109/JSTARS.2015.2446611.
- Lamsal, D., T. Sawagaki, and T. Watanabe. 2011. “Digital Terrain Modelling Using Corona and ALOS PRISM Data to Investigate the Distal Part of Imja Glacier, Khumbu Himal, Nepal.” Journal of Mountain Science 8 (3): 390–402. SP Science Press. doihttps://doi.org/10.1007/s11629-011-2064-0.
- LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. “Gradient-Based Learning Applied to Document Recognition.” Proceedings of the IEEE 86 (11): 2278–2324. doi:https://doi.org/10.1109/5.726791.
- LeCun, Y., and Y. Bengio. 1995. “Convolutional Networks for Images, Speech, and Time Series.” In The Handbook of Brain Theory and Neural Networks, edited by Michael A. Arbib, 255–258. MIT Press.
- Lv, X., D. Ming, L. Tingting, K. Zhou, M. Wang, and H. Bao. 2018. “A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification.” Remote Sensing 10 (12): 1946. doi:https://doi.org/10.3390/rs10121946.
- Lv, X., D. Ming, Y. Y. Chen, and M. Wang. 2019. “Very High Resolution Remote Sensing Image Classification with SEEDS-CNN and Scale Effect Analysis for Superpixel CNN Classification.” International Journal of Remote Sensing 40 (2): 506–531. Taylor & Francis. doihttps://doi.org/10.1080/01431161.2018.1513666.
- Mboga, N., T. Grippa, S. Georganos, S. Vanhuysse, B. Smets, O. Dewitte, E. Wolff, and M. Lennert. 2020. “Fully Convolutional Networks for Land Cover Classification from Historical Panchromatic Aerial Photographs.”ISPRS Journal of Photogrammetry and Remote Sensing 167 (September). Elsevier B.V. 385–395 doi:https://doi.org/10.1016/j.isprsjprs.2020.07.005.
- Mir, R. A., and Z. Majeed. 2018. “Frontal Recession of Parkachik Glacier between 1971-2015, Zanskar Himalaya Using Remote Sensing and Field Data.” Geocarto International 33 (2): 163–177. doi:https://doi.org/10.1080/10106049.2016.1232439.
- Nasr, G. E., E. A. Badr, and C. Joun. 2002. “Cross Entropy Error Function in Neural Networks: Forecasting Gasoline Demand.” In FLAIRS Conference, 381–384. Florida, USA.
- Nian, Y., L. Xin, and J. Zhou. 2017. “Landscape Changes of the Ejin Delta in the Heihe River Basin in Northwest China from 1930 to 2010.” International Journal of Remote Sensing 38 (2): 537–557. doi:https://doi.org/10.1080/01431161.2016.1268732.
- Nita, M. D., C. Munteanu, G. Gutman, I. V. Abrudan, and V. C. Radeloff. 2018. “Widespread Forest Cutting in the Aftermath of World War 2 Captured by Broad-Scale Historical Corona Spy Satellite Photography.” Remote Sensing of Environment 204 (January): 322–332. doi:https://doi.org/10.1016/j.rse.2017.10.021.
- Paul, F., T. Strozzi, T. Schellenberger, and K. Andreas. 2017. “The 2015 Surge of Hispar Glacier in the Karakoram.” Remote Sensing 9 (9): 888. doi:https://doi.org/10.3390/rs9090888.
- Pedregosa, F., G. Olivier, W. Ron, P. Alexandre, B. Matthieu, G. Varoquax, G. Alexandre et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12:2825–2830.
- Qureshi, M. A., Y. Chaolu, X. Xiangke, and L. Yingkui. 2017. “Glacier Status during the Period 1973–2014 in the Hunza Basin, Western Karakoram.” Quaternary International 444 (July): 125–136. doi:https://doi.org/10.1016/j.quaint.2016.08.029.
- Renard, K. G., G. R. Foster, G. A. Weesies, D. K. McCool, and D. C. Yoder. 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE).., USDA-ARS Agriculture Handbook 703 . Washington, DC: U.S. Government Printing Office.
- Robson, B. A., P. R. Christopher Nuth, L. G. Nielsen, M. Hendrickx, and S. O. Dahl. 2018. “Spatial Variability in Patterns of Glacier Change across the Manaslu Range, Central Himalaya.” Frontiers in Earth Science 6 (February). doi:https://doi.org/10.3389/feart.2018.00012.
- She, J., Y. Zhang, L. Xingong, and Y. Chen. 2014. “Changes in Snow and Glacier Cover in an Arid Watershed of the Western Kunlun Mountains Using Multisource Remote-Sensing Data.” International Journal of Remote Sensing 35 (1): 234–252. doi:https://doi.org/10.1080/01431161.2013.866296.
- Simonyan, K., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” ICLR, 1–14. http://arxiv.org/abs/1409.1556
- Song, D. X., J. O. Chengquan Huang, S. C. Sexton, M. Feng, and J. R. Townshend. 2015. “Use of Landsat and Corona Data for Mapping Forest Cover Change from the Mid-1960s to 2000s: Case Studies from the Eastern United States and Central Brazil.” ISPRS Journal of Photogrammetry and Remote Sensing 103: 81–92. May. Elsevier. doi:https://doi.org/10.1016/j.isprsjprs.2014.09.005.
- Song, W., L. Shutao, L. Fang, and L. Ting. 2018. “Hyperspectral Image Classification With Deep Feature Fusion Network.” IEEE Transactions on Geoscience and Remote Sensing 56 (6): 3173–3184. IEEE. doihttps://doi.org/10.1109/TGRS.2018.2794326.
- Taigman, Y., M. Yang, R. Marc’Aurelio, and L. Wolf. 2014. “DeepFace: Closing the Gap to Human-Level Performance in Face Verification.” In 2014 IEEE Conference on Computer Vision and Pattern Recognition, 1701–1708. Columbus, OH: IEEE. doi:https://doi.org/10.1109/CVPR.2014.220.
- Tarabalka, Y., J. A. Benediktsson, and J. Chanussot. 2009. “Spectral-Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques.” IEEE Transactions on Geoscience and Remote Sensing 47 (8): 2973–2987. IEEE. doihttps://doi.org/10.1109/TGRS.2009.2016214.
- Tielidze, L. G., and R. D. Wheate. 2018. “The Greater Caucasus Glacier Inventory (Russia, Georgia and Azerbaijan).” The Cryosphere 12 (1): 81–94. doi:https://doi.org/10.5194/tc-12-81-2018.
- Tuceryan, M. 1994. “Moment-Based Texture Segmentation.” Pattern Recognition Letters 15 (7): 659–668. North-Holland. doihttps://doi.org/10.1016/0167-8655(94)90069-8.
- Zeiler, M. D., and R. Fergus. 2014. “Visualizing and Understanding Convolutional Networks.” In European Conference on Computer Vision (ECCV), 8689:818–833. Zurich, Switzerland. doi:https://doi.org/10.1007/978-3-319-10590-1_53.
- Zhao, W., and D. Shihong. 2016. “Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach.” IEEE Transactions on Geoscience and Remote Sensing 54 (8): 4544–4554. IEEE. doihttps://doi.org/10.1109/TGRS.2016.2543748.