Change detection in very high-resolution images based on ensemble CNNs

References

• Bazi, Y., L. Bruzzone, and F. Melgani. 2005. “An Unsupervised Approach Based on the Generalized Gaussian Model to Automatic Change Detection in Multitemporal SAR Images.” IEEE Transactions on Geoscience and Remote Sensing 43 (4): 874–887. doi:10.1109/tgrs.2004.842441.
   Web of Science ®Google Scholar
• Bontemps, S., A. Langner, and P. Defourny. 2012. “Monitoring Forest Changes in Borneo on a Yearly Basis by an Object-based Change Detection Algorithm Using SPOT-VEGETATION Time Series.” International Journal of Remote Sensing 33 (15): 4673–4699. doi:10.1080/01431161.2011.638336.
   Web of Science ®Google Scholar
• Bouziani, M., K. Goïta, and D. C. He. 2010. “Automatic Change Detection of Buildings in Urban Environment from Very High Spatial Resolution Images Using Existing Geodatabase and Prior Knowledge.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 143–153. doi:10.1016/j.isprsjprs.2009.10.002.
   Web of Science ®Google Scholar
• Chen, G., G. J. Hay, L. M. Carvalho, and M. A. Wulder. 2012. “Object-based Change Detection.” International Journal of Remote Sensing 33 (14): 4434–4457. doi:10.1007/s00500-014-1460-0.
   Web of Science ®Google Scholar
• Dingle Robertson, L., and D. J. King. 2011. “Comparison of Pixel-and Object-based Classification in Land Cover Change Mapping.” International Journal of Remote Sensing 32 (6): 1505–1529. doi:10.1080/01431160903571791.
   Web of Science ®Google Scholar
• Dong, Y., B. Du, L. Zhang, and L. Zhang. 2017. “Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning.” IEEE Transactions on Geoscience and Remote Sensing 55 (5): 2509–2524. doi:10.1109/TGRS.2016.2645703.
   Web of Science ®Google Scholar
• Fisher, P. 1997. “The Pixel: A Snare and a Delusion.” International Journal of Remote Sensing 18 (3): 679–685. doi:10.1080/014311697219015.
   Web of Science ®Google Scholar
• Gao, F., E. B. de Colstoun, R. Ma, Q. Weng, J. G. Masek, J. Chen, and C. Song. 2012. “Mapping Impervious Surface Expansion Using Medium-resolution Satellite Image Time Series: A Case Study in the Yangtze River Delta, China.” International Journal of Remote Sensing 33 (24): 7609–7628. doi:10.1080/01431161.2012.700424.
   Web of Science ®Google Scholar
• Gong, M., H. Yang, and P. Zhang. 2017. “Feature Learning and Change Feature Classification Based on Deep Learning for Ternary Change Detection in SAR Images.” ISPRS Journal of Photogrammetry and Remote Sensing 129: 212–225. doi:10.1016/j.isprsjprs.2017.05.001.
   Web of Science ®Google Scholar
• Gong, M., J. Zhao, J. Liu, Q. Miao, and L. Jiao. 2015. “Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks.” IEEE Transactions on Neural Networks and Learning Systems 27 (1): 125–138. doi:10.1109/TNNLS.2015.2435783.
   PubMed Web of Science ®Google Scholar
• Habib, T., J. Inglada, G. Mercier, and J. Chanussot. 2009. “Support Vector Reduction in SVM Algorithm for Abrupt Change Detection in Remote Sensing.” IEEE Geoscience and Remote Sensing Letters 6 (3): 606–610. doi:10.1109/lgrs.2009.2020306.
   Web of Science ®Google Scholar
• Hinton, G. E. 2012. “A Practical Guide to Training Restricted Boltzmann Machines.” Neural Networks: Tricks of the Trade 4:: 599–619. doi:10.1145/3065386.
   Google Scholar
• Hinton, G. E., and R. R. Salakhutdinov. 2006. “Reducing the Dimensionality of Data with Neural Networks.” science 313 (5786): 504–507. doi:10.1126/science.1127647.
   PubMed Web of Science ®Google Scholar
• Hore, A., and D. Ziou. 2010. “Image Quality Metrics: PSNR Vs. SSIM.” Pattern Recognition 1: 2366–2369. doi:10.1109/ICPR.2010.579.
   Google Scholar
• Huang, F., C. Xu, and Z. Zhu. 2016. “Change Detection of Hyperspectral Remote Sensing Images Based on Deep Belief Network.” Change 9 (05): 2096–2105.
   Google Scholar
• Hussain, M., D. Chen, A. Cheng, H. Wei, and D. Stanley. 2013. “Change Detection from Remotely Sensed Images: From Pixel-based to Object-based Approaches.” ISPRS Journal of Photogrammetry and Remote Sensing 80: 91–106. doi:10.1016/j.isprsjprs.2013.03.006.
   Web of Science ®Google Scholar
• Im, J., and J. R. Jensen. 2005. “A Change Detection Model Based on Neighborhood Correlation Image Analysis and Decision Tree Classification.” Remote Sensing of Environment 99 (3): 326–340. doi:10.1016/j.rse.2005.09.008.
   Web of Science ®Google Scholar
• Khan, S. H., X. He, F. Porikli, and M. Bennamoun. 2017. “Forest Change Detection in Incomplete Satellite Images with Deep Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing 55 (9): 5407–5423. doi:10.1109/tgrs.2017.2707528.
   Web of Science ®Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, United States, December 3–6. doi: 10.1145/3065386.
   Google Scholar
• Lan, Z., and L. Yang. 2018. “Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge.” ISPRS International Journal of Geo-Information 7 (5): 175–199. doi:10.3390/ijgi7050175.
   Web of Science ®Google Scholar
• Listner, C., and I. Niemeyer. 2011. “Object-based Change Detection.” Photogrammetrie-Fernerkundung- Geoinformation 4: 233–245. doi:10.1127/1432-8364/2011/0085.
   Google Scholar
• Liu, J., M. Gong, J. Zhao, H. Li, and L. Jiao. 2016. “Difference Representation Learning Using Stacked Restricted Boltzmann Machines for Change Detection in SAR Images.” Soft Computing 20 (12): 4645–4657.
   Web of Science ®Google Scholar
• Lyu, H., H. Lu, L. Mou, W. Li, J. Wright, X. Li, … P. Gong. 2018. “Long-term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data.” Remote Sensing 10 (3): 471–494. doi:10.3390/rs10030471.
   Web of Science ®Google Scholar
• Mignon, A., and F. Jurie. 2012. “Pcca: A New Approach for Distance Learning from Sparse Pairwise Constraints.” Computer Vision and Pattern Recognition 3: 2666–2672. doi:10.1109/CVPR.2012.6247987.
   Google Scholar
• Rosenfield, G. H., and K. Fitzpatrick-Lins. 1986. “A Coefficient of Agreement as A Measure of Thematic Classification Accuracy.” Photogrammetric Engineering and Remote Sensing 52 (2): 223–227.
   Web of Science ®Google Scholar
• Rosin, P. L., and E. Ioannidis. 2003. “Evaluation of Global Image Thresholding for Change Detection.” Pattern Recognition Letters 24 (14): 2345–2356. doi:10.1016/s0167-8655(03)00060-6.
   Web of Science ®Google Scholar
• Russakovsky, O., J. Deng, S. Hao, J. Krause, S. Satheesh, M. Sean, Z. Huang, A. Karpathy, A. Khosla, and M. Bernstein. 2014. “ImageNet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115 (3): 211–252. doi:10.1007/s11263-015-0816-y.
   Web of Science ®Google Scholar
• Simonyan, K., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large-scale Image Recognition”. [Online]. Available:https://arxiv.org/abs/1409.1556
   Google Scholar
• Sofina, N., and M. Ehlers. 2016. “Building Change Detection Using High Resolution Remotely Sensed Data and GIS.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (8): 3430–3438. doi:10.1109/JSTARS.2016.2542074.
   Web of Science ®Google Scholar
• Su, L., M. Gong, P. Zhang, M. Zhang, J. Liu, and H. Yang. 2017. “Deep Learning and Mapping Based Ternary Change Detection for Information Unbalanced Images.” Pattern Recognition 66: 213–228. doi:10.1016/j.patcog.2017.01.002.
   Web of Science ®Google Scholar
• Teo, T. A., and T. Y. Shih. 2013. “Lidar-based Change Detection and Change-type Determination in Urban Areas.” International Journal of Remote Sensing 34 (3): 968–981. doi:10.1080/01431161.2012.714504.
   Web of Science ®Google Scholar
• Tewkesbury, A. P., A. J. Comber, N. J. Tate, A. Lamb, and P. F. Fisher. 2015. “A Critical Synthesis of Remotely Sensed Optical Image Change Detection Techniques.” Remote Sensing of Environment 160: 1–14. doi:10.1016/j.rse.2015.01.006.
   Web of Science ®Google Scholar
• Thonfeld, F., H. Feilhauer, M. Braun, and G. Menz. 2016. “Robust Change Vector Analysis (RCVA) for Multi-sensor Very High Resolution Optical Satellite Data.” International Journal of Applied Earth Observation and Geoinformation 50: 131–140. doi:10.1016/j.jag.2016.03.009.
   Web of Science ®Google Scholar
• Wang, Q., X. Zhang, G. Chen, F. Dai, Y. Gong, and K. Zhu. 2018. “Change Detection Based on Faster R-CNN for High-resolution Remote Sensing Images.” Remote Sensing Letters 9 (10): 923–932. doi:10.1016/j.isprsjprs.2017.05.001.
   Web of Science ®Google Scholar
• Wang, Y., B. R. Mitchell, J. Nugranad-Marzilli, G. Bonynge, Y. Zhou, and G. Shriver. 2009. “Remote Sensing of Land-cover Change and Landscape Context of the National Parks: A Case Study of the Northeast Temperate Network.” Remote Sensing of Environment 113 (7): 1453–1461. doi:10.1016/j.rse.2008.09.017.
   Web of Science ®Google Scholar
• Wang, Z., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. “Image Quality Assessment: From Error Visibility to Structural Similarity.” IEEE Transactions on Image Processing 13 (4): 600–612. doi:10.1109/TIP.2003.819861.
   PubMed Web of Science ®Google Scholar
• Warner, T. 2011. “Kernel Based Texture in Remote Sensing Image Classification.” Geography Compass 5 (10): 781–798. doi:10.1111/j.1749-8198.2011.00451.x.
   Google Scholar
• Zhang, F., B. Du, and L. Zhang. 2014. “Saliency-guided Unsupervised Feature Learning for Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing 53 (4): 2175–2184. doi:10.1109/tgrs.2014.2357078.
   Web of Science ®Google Scholar
• Zhang, Q., L. Zhang, Y. Yang, Y. Tian, and L. Weng. 2013. “Local Patch Discriminative Metric Learning for Hyperspectral Image Feature Extraction.” IEEE Geoscience and Remote Sensing Letters 11 (3): 612–616. doi:10.1109/LGRS.2013.2272476.
   Web of Science ®Google Scholar