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Original Articles

A decision-based multi-sensor classification system using thermal hyperspectral and visible data in urban area

, &
Pages 414-427 | Received 18 Feb 2017, Accepted 26 Jun 2017, Published online: 12 Jul 2017

References

  • Abe, S. (2010). Support vector machines for pattern classification. Springer Science & Business Media. doi:10.1007/978-1-84996-098-4
  • Baatz, M., & Arno, S. (2000). Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung XII, 58, 12–427.
  • Bigdeli, B., Samadzadegan, F., & Reinartz, P. (2013). A multiple SVM system for classification of hyperspectral remote sensing data. Journal of the Indian Society of Remote Sensing, 41(4), 763–776. doi:10.1007/s12524-013-0286-z
  • Bigdeli, B., Samadzadegan, F., & Reinartz, P. (2014). A decision fusion method based on multiple support vector machine system for fusion of hyperspectral and LIDAR data. International Journal of Image and Data Fusion, 5(3), 196–209. doi:10.1080/19479832.2014.919964
  • Blanchart, P., Ferecatu, M., Cui, S., & Datcu, M. (2014). Pattern retrieval in large image databases using multiscale coarse-to-fine cascaded active learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4), 1127–1141. doi:10.1109/JSTARS.2014.2302333
  • Bruzzone, L., & Bovolo, F. (2013). A novel framework for the design of change-detection systems for very-high-resolution remote sensing images. Proceedings of the IEEE, 101(3), 609–630. doi:10.1109/JPROC.2012.2197169
  • Bruzzone, L., & Marconcini, M. (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
  • Camps-Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J.A. (2014). Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE Signal Processing Magazine, 31(1), 45–54. doi:10.1109/MSP.2013.2279179
  • Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27. doi:10.1145/1961189.1961199
  • Crawford, M.M., Tuia, D., & Yang, H.L. (2013). Active learning: Any value for classification of remotely sensed data? Proceedings of the IEEE, 101(3), 593–608. doi:10.1109/JPROC.2012.2231951
  • Du, P., Liu, S., Xia, J., & Zhao, Y. (2013). Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion, 14(1), 19–27. doi:10.1016/j.inffus.2012.05.003
  • Eslami, M., & Mohammadzadeh, A. (2015). Developing a spectral-based strategy for urban object detection from airborne hyperspectral TIR and visible data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5), 1808–1816. doi:10.1109/JSTARS.2015.2489838
  • Espinoza-Molina, D., & Datcu, M. (2013). Earth-observation image retrieval based on content, semantics, and metadata. IEEE Transactions on Geoscience and Remote Sensing, 51(11), 5145–5159. doi:10.1109/TGRS.2013.2262232
  • Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J., & Tilton, J.C. (2013). Advances in spectral-spatial classification of hyperspectral images. Proceedings of the IEEE, 101(3), 652–675. doi:10.1109/JPROC.2012.2197589
  • Guo, M., Zhang, H., Li, J., Zhang, L., & Shen, H. (2014). An online coupled dictionary learning approach for remote sensing image fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4), 1284–1294. doi:10.1109/JSTARS.2014.2310781
  • Haralick, R.M., Shanmugam, K., et al. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621. doi:10.1109/TSMC.1973.4309314
  • Hasanlou, M., Samadzadegan, F., & Homayouni, S. (2015). SVM-based hyperspectral image classification using intrinsic dimension. Arabian Journal of Geosciences, 8(1), 477–487. doi:10.1007/s12517-013-1141-9
  • Huang, X., & Zhang, L. (2012a). A multilevel decision fusion approach for urban mapping using very high-resolution multi/hyperspectral imagery. International Journal of Remote Sensing, 33(11), 3354–3372. doi:10.1080/01431161.2011.591444
  • Huang, X., & Zhang, L. (2012b). Morphological building/shadow index for building extraction from high-resolution imagery over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(1), 161–172. doi:10.1109/JSTARS.2011.2168195
  • Huang, X., Zhang, L., & Zhu, T. (2014). Building change detection from multitemporal high-resolution remotely sensed images based on a morphological building index. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1), 105–115. doi:10.1109/JSTARS.2013.2252423
  • Kuncheva, L.I. (2004). Combining pattern classifiers: Methods and algorithms. John Wiley & Sons. doi:10.1002/0471660264
  • Li, J., Zhang, H., Guo, M., Zhang, L., Shen, H., & Du, Q. (2015). Urban classification by the fusion of thermal infrared hyperspectral and visible data. Photogrammetric Engineering & Remote Sensing, 81(12), 901–911. doi:10.14358/PERS.81.12.901
  • Li, S., Wu, H., Wan, D., & Zhu, J. (2011). An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine. Knowledge-Based Systems, 24(1), 40–48. doi:10.1016/j.knosys.2010.07.003
  • Liao, W., Huang, X., Van Coillie, F., Gautama, S., Pizurica, A., Philips, W., … Tuia, D. (2015). Processing of multiresolution thermal hyperspectral and digital color data: Outcome of the 2014 IEEE GRSS data fusion contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 2984–2996. doi:10.1109/JSTARS.2015.2420582
  • Lu, X., Zhang, J., Li, T., & Zhang, G. (2015). Synergetic classification of long-wave infrared hyperspectral and visible images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7), 3546–3557. doi:10.1109/JSTARS.2015.2442594
  • Miliaresis, G.C. (2014). Daily temperature oscillation enhancement of multitemporal LST imagery. Photogrammetric Engineering & Remote Sensing, 80(5), 423–428. doi:10.14358/PERS.80.5.423
  • Moser, G., Serpico, S.B., & Benediktsson, J.A. (2013). Land-cover mapping by markov modeling of spatial–contextual information in very-high-resolution remote sensing images. Proceedings of the IEEE, 101(3), 631–651. doi:10.1109/JPROC.2012.2211551
  • Persello, C., & Bruzzone, L. (2012). Active learning for domain adaptation in the supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 50(11), 4468–4483. doi:10.1109/TGRS.2012.2192740
  • Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-Valls, G., … Trianni, G. (2009). Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113, S110–S122. doi:10.1016/j.rse.2007.07.028
  • Rodríguez-Galiano, V.F., Ghimire, B., Pardo-Igúzquiza, E., Chica-Olmo, M., & Congalton, R.G. (2012). Incorporating the downscaled Landsat TM thermal band in land-cover classification using random forest. Photogrammetric Engineering & Remote Sensing, 78(2), 129–137. doi:10.14358/PERS.78.2.129
  • Rogova, G. (1994). Combining the results of several neural network classifiers. Neural Networks, 7(5), 777–781. doi:10.1016/0893-6080(94)90099-X
  • Thomas, C., Ranchin, T., Wald, L., & Chanussot, J. (2008). Synthesis of multispectral images to high spatial resolution: A critical review of fusion methods based on remote sensing physics. IEEE Transactions on Geoscience and Remote Sensing, 46(5), 1301–1312. doi:10.1109/TGRS.2007.912448
  • Tian, J., & Reinartz, P. (2011). Multitemporal 3D change detection in urban areas using stereo information from different sensors. 2011 International Symposium on Image and Data Fusion (ISIDF) (pp. 1–4). doi:10.1109/ISIDF.2011.6024215
  • Tuia, D., Volpi, M., Copa, L., Kanevski, M., & Munoz-Mari, J. (2011). A survey of active learning algorithms for supervised remote sensing image classification. IEEE Journal of Selected Topics in Signal Processing, 5(3), 606–617. doi:10.1109/JSTSP.2011.2139193
  • Tuia, D., Volpi, M., Trolliet, M., & Camps-Valls, G. (2014). Semisupervised manifold alignment of multimodal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 52(12), 7708–7720. doi:10.1109/TGRS.2014.2317499
  • Voisin, A., Krylov, V.A., Moser, G., Serpico, S.B., & Zerubia, J. (2014). Supervised classification of multisensor and multiresolution remote sensing images with a hierarchical copula-based approach. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3346–3358. doi:10.1109/TGRS.2013.2272581
  • Wald, L. (1999). Some terms of reference in data fusion. IEEE Transactions on Geoscience and Remote Sensing, 37(3), 1190–1193. doi:10.1109/36.763269
  • Wang, N., Wu, H., Nerry, F., Li, C., & Li, Z.-L. (2011). Temperature and emissivity retrievals from hyperspectral thermal infrared data using linear spectral emissivity constraint. IEEE Transactions on Geoscience and Remote Sensing, 49(4), 1291–1303. doi:10.1109/TGRS.2010.2062527
  • Wemmert, C., Puissant, A., Forestier, G., & Gancarski, P. (2009). Multiresolution remote sensing image clustering. IEEE Geoscience and Remote Sensing Letters, 6(3), 533–537. doi:10.1109/LGRS.2009.2020825
  • Winter, E.M. (2004). Endmember-Based in-Scene Atmospheric Retrieval (EMISAR). In Proceedings. 2004 IEEE aerospace conference, 2004 (Vol. 3). IEEE. doi:10.1109/AERO.2004.1367962