Unsupervised change detection in high spatial resolution remote sensing images based on a conditional random field model

References

• Baiocchi V., Brigante R., Dominici D., Milone M.V., Mormile M., Radicioni F. (2014)—Automatic three-dimensional features extraction: The case study of L'Aquila for collapse identification after April 06, 2009 earthquake. European Journal of Remote Sensing, 47: 413–435, doi: http://dx.doi.org/10.5721/EuJRS20144724.
   Web of Science ®Google Scholar
• Bazi Y., Bruzzone L., Melgani F. (2010)—Unsupervised Change Detection in Multispectral Remotely Sensed Imagery with Level Set Methods. IEEE Transactions on Geoscience and Remote Sensing, 48 (8): 3178–3187, doi: http://dx.doi.org/10.1109/tgrs.2010.2045506.
   Google Scholar
• Benson K.K., Valentyn A.T., Alfred S. (2014)—Detection of built-up area in optical and synthetic aperture radar images using conditional random fields. Journal of Applied Remote Sensing, 8 (1): 083672-1-18. doi: http://dx.doi.org/10.1117/1JRS.8.083672.
   Google Scholar
• Bovolo F., Bruzzone L. (2007)—A split-based approach to unsupervised change detection in large-size multitemporal images: Application to tsunami-damage assessment. IEEE Transactions on Geoscience and Remote Sensing, 45 (6): 1658–1670, doi: http://dx.doi.org/10.1109/TGRS.2007.895835.
   Google Scholar
• Bruzzone L., Prieto D. (2000)—Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing,38 (3):1171–1182. doi: http://dx.doi.org/10.1109/36.843009.
   Web of Science ®Google Scholar
• Bruzzone L., Prieto D. (2002)—An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images. IEEE Transactions on Image Processing, 11: 452–466. doi: http://dx.doi.org/10.1109/TIP.2002.999678.
   PubMed Web of Science ®Google Scholar
• Carlo M., Bovolo F., Bruzzone L. (2015)—Building change detection in multitemporal very high resolution SAR images. IEEE Transactions on Geoscience and Remote Sensing, 53 (5): 2664–2682. doi: http://dx.doi.org/10.1109/TGRS.2014.2363548.
   Google Scholar
• Celik T. (2009)—Unsupervised change detection in satellite images using principal component analysis and K-means clustering. IEEE Geoscience and Remote Sensing Letters, 6 (4): 772–776. doi: http://dx.doi.org/10.1109/LGRS.2009.2025059.
   Web of Science ®Google Scholar
• Celik T. (2010)—A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images. Signal Processing, 90: 1471–1485. doi: http://dx.doi.org/10.1016/j.sigpro.2009.10.018.
   Web of Science ®Google Scholar
• Celik T., Ma K.K. (2011)—Multitemporal image change detection using undecimated discrete wavelet transform and active contours. IEEE Transactions on Geoscience and Remote Sensing, 49 (2): 706–716. doi: http://dx.doi.org/10.1109/TGRS.2010.2066979.
   Google Scholar
• Chen Y., Cao Z. (2013)—An improved MRF-based change detection approach for multitemporal remote sensing imagery. Signal Processing, 93: 163–175. doi: http://dx.doi.org/10.1016/j.sigpro.2012.07.013.
   Web of Science ®Google Scholar
• Fichera Carmelo R., Modica G., Pollino M. (2012)—Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. European Journal of Remote Sensing, 45: 1–18. doi: http://dx.doi.org/10.5721/EuJRS20124501.
   Google Scholar
• Foody G.M. (2004)—Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering and Remote Sensing, 70: 627–633. doi: http://dx.doi.org/10.14358/PERS.70.5.627.
   Web of Science ®Google Scholar
• Gong M., Li Y., Jiao L., Jia M., Su L. (2014)—SAR change detection based on intensity and texture changes. ISPRS Journal of Photogrammetry and Remote Sensing, 93:123–135. doi: http://dx.doi.org/10.1016/jisprsjprs.2014.04.010.
   Web of Science ®Google Scholar
• Hao M., Zhang H., Shi W., Deng K. (2013)—Unsupervised change detection using fuzzy c-means and MRF from remotely sensed images. Remote Sensing Letters, 4 (12): 1185–1194. doi: http://dx.doi.org/10.1080/2150704X.2013.858841.
   Web of Science ®Google Scholar
• Hao M., Shi W., Deng K., Zhang H. (2014)—A contrast-sensitive Potts model custom-designed for change detection. European Journal of Remote Sensing, 47: 634–654. doi: http://dx.doi.org/10.5721/EuJRS20144736.
   Google Scholar
• Hoberg T., Rottensteiner F., Feitosa Queiroz R., Heipke C. (2014)—Conditional Random Fields for Multitemporal and Multiscale Classification of Optical Satellite Imagery, IEEE Transactions on Geoscience and Remote Sensing, 53 (2): 659–673, doi: http://dx.doi.org/10.1109/TGRS.2014.2326886.
   Web of Science ®Google Scholar
• Hou B., Wei Q., Zheng Y., Wang S. (2014)—Unsupervised change detection in SAR image based on Gauss-Log ratio image fusion and compressed projection. IEEE Transactions on Geoscience and Remote Sensing, 7 (8): 3297–3315. doi: http://dx.doi.org/10.1109/JSTARS.2014.2328344.
   Google Scholar
• Huo C., Zhou Z., Lu H., Pan C., Chen K. (2010)—Fast object-level change detection for VHR images. IEEE Geoscience and Remote Sensing Letters, 7 (1): 118–122. doi: http://dx.doi.org/10.1109/LGRS.2009.2028438.
   Google Scholar
• Kosov S. (2013)—Direct graphical models C++ library. Available online at: http://research.project-10.de/dgm/.
   Google Scholar
• Kumar S., Hebert M. (2003)—Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification. Proceedings of 9^th IEEE International Conference on Computer Vision, Nice, France, 2: 1150–1157. doi: http://dx.doi.org/10.1109/ICCV.2003.1238478.
   Google Scholar
• Lafferty J., McCallum A., Pereira F.C.N. (2001)—Conditional random fields: Probabilistic models for segmenting and labeling sequence data, Proceedings of 18^th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA, 282–289. Available online at http://portal.acm.org/citation.cfm?id=655813.
   Google Scholar
• Miao L., Shuying Z., Bing Z., Shanshan L., Changshan W. (2014)—A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information. European Journal of Remote Sensing, 47: 389–411. doi: http://dx.doi.org/10.5721/EuJRS20144723.
   Google Scholar
• Otsu N. (1979)—A threshold selection method from gray-level histogram. IEEE Transactions on System, Man, Cybernetics, SMC-9 (1): 62–66. doi: http://dx.doi.org/10.1109/TSMC.1979.4310076.
   Web of Science ®Google Scholar
• Smiraglia D., Rinaldo S. Ceccarelli T., Bajocco S., Salvati L., Ricotta C., Perini L. (2014)—A cost-effective approach for improving the quality of soil sealing change detection from Landsat Imagery. European Journal of Remote Sensing, 47: 805–819. doi: http://dx.doi.org/10.5721/EuJRS20144746.
   Web of Science ®Google Scholar
• Volpi M., Tuia D., Camps-Valls G., Kanevski M. (2012)—Unsupervised Change Detection with Kernels. IEEE Geoscience and Remote Sensing Letters, 9 (6): 1026–1030. doi: http://dx.doi.org/10.1109/LGRS.2012.2189092.
   Google Scholar
• Wang B., Seokkeun C., Younggi B., Soungki L., Jaewan C. (2015)—Object-based change detection of very high resolution satellite imagery using the cross-sharpening of multitemporal data. IEEE Geoscience and Remote Sensing Letters, 12 (5): 1151–1155. doi: http://dx.doi.org/10.1109/LGRS.2014.2386878.
   Google Scholar
• Yang X., Chen L. (2010)—Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides. International Journal of Applied Earth Observation and Geoinfomation, 12 (6): 487–495. doi: http://dx.doi.org/10.1016/jjag.2010.05.006.
   Web of Science ®Google Scholar
• Yu L., Xie J., Chen S. (2012)—Conditional random field-based image labelling combining features of pixels, segments and regions. IET Computer Vision, 6(5): 459–467, doi: http://dx.doi.org/10.1049/iet-cvi.2011.0203.
   Google Scholar
• Zewdie W., Csaplovics E. (2015)—Remote Sensing based multi-temporal land cover classification and change detection in northwestern Ethiopia. European Journal of Remote Sensing, 48: 121–139. doi: http://dx.doi.org/10.5721/EuJRS20154808.
   Web of Science ®Google Scholar
• Zhang G., Jia X. (2012)—Simplified conditional random fields with class boundary constraint for spectral-spatial based remote sensing image classification. IEEE Geoscience and Remote Sensing Letters, 9 (5): 856–860. doi: http://dx.doi.org/10.1109/LGRS.2012.2186279.
   Google Scholar
• Zhong Y., Zhao J., Zhang L. (2014)—A hybrid object-oriented conditional random field classification framework for high spatial resolution remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 52 (11): 7023–7037. doi: http://dx.doi.org/10.1109/TGRS.2014.2306692.
   Google Scholar
• Zhong P., Wang R. (2014)—Jointly Learning the Hybrid CRF and MLR Model for Simultaneous Denoising and Classification of Hyperspectral Imagery, IEEE Transactions on Neural Networks and Learning Systems, 25 (7): 1319–1334. doi: http://dx.doi.org/10.1109/TNNLS.2013.2293061.
   Google Scholar