1. [1] G. G. Wilkinson, "Results and implications of a study of fifteen years of satellite image classification experiments," Geoscience and Remote Sensing, IEEE Transactions on, vol. 43, pp. 433-440, 2005. [ DOI:10.1109/TGRS.2004.837325] 2. [2] A. Hadavand, M. Saadatseresht, and S. Homayouni, "A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER," The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 40, p. 263, 2015. [ DOI:10.5194/isprsarchives-XL-1-W5-263-2015] 3. [3] G. Schumann, R. Hostache, C. Puech, L. Hoffmann, P. Matgen, F. Pappenberger, et al., "High-resolution 3-D flood information from radar imagery for flood hazard management," Geoscience and Remote Sensing, IEEE Transactions on, vol. 45, pp. 1715-1725, 2007. [ DOI:10.1109/TGRS.2006.888103] 4. [4] Y. Du, P. M. Teillet, and J. Cihlar, "Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection," Remote sensing of Environment, vol. 82, pp. 123-134, 2002. [ DOI:10.1016/S0034-4257(02)00029-9] 5. [5] C. Zhang and J. M. Kovacs, "The application of small unmanned aerial systems for precision agriculture: a review," Precision agriculture, vol. 13, pp. 693-712, 2012. [ DOI:10.1007/s11119-012-9274-5] 6. [6] N. R. Pal and S. K. Pal, "A review on image segmentation techniques," Pattern recognition, vol. 26, pp. 1277-1294, 1993. [ DOI:10.1016/0031-3203(93)90135-J] 7. [7] O. Veksler, Y. Boykov, and P. Mehrani, "Superpixels and supervoxels in an energy optimization framework," in Computer Vision–ECCV 2010, ed: Springer, 2010, pp. 211-224. [ DOI:10.1007/978-3-642-15555-0_16] 8. [8] V. Dey, Y. Zhang, and M. Zhong, A review on image segmentation techniques with remote sensing perspective: na, 2010. 9. [9] S. Sahli, P.-L. Duval, Y. Sheng, and D. A. Lavigne, "Robust vehicle detection in aerial images based on salient region selection and superpixel classification," in SPIE Defense, Security, and Sensing, 2011, pp. 80200L-80200L-11. 10. [10] G. Zhang, X. Jia, and N. M. Kwok, "Super pixel based remote sensing image classification with histogram descriptors on spectral and spatial data," in Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, 2012, pp. 4335-4338. [ DOI:10.1109/IGARSS.2012.6351708] 11. [11] L. Fang, S. Li, X. Kang, and J. A. Benediktsson, "Spectral–Spatial Classification of Hyperspectral Images With a Superpixel-Based Discriminative Sparse Model," Geoscience and Remote Sensing, IEEE Transactions on, vol. 53, pp. 4186-4201, 2015. [ DOI:10.1109/TGRS.2015.2392755] 12. [12] G. Zhang, X. Jia, and J. Hu, "Superpixel-Based Graphical Model for Remote Sensing Image Mapping," Geoscience and Remote Sensing, IEEE Transactions on, vol. 53, pp. 5861-5871, 2015. [ DOI:10.1109/TGRS.2015.2423688] 13. [13] J. Shi and J. Malik, "Normalized cuts and image segmentation," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, pp. 888-905, 2000. [ DOI:10.1109/34.868688] 14. [14] D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, pp. 603-619, 2002. [ DOI:10.1109/34.1000236] 15. [15] L. Vincent and P. Soille, "Watersheds in digital spaces: an efficient algorithm based on immersion simulations," IEEE Transactions on Pattern Analysis & Machine Intelligence, pp. 583-598, 1991. [ DOI:10.1109/34.87344] 16. [16] A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, "Turbopixels: Fast superpixels using geometric flows," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 31, pp. 2290-2297, 2009. [ DOI:10.1109/TPAMI.2009.96] 17. [17] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, "Slic superpixels," 2010. 18. [18] J. MacQueen, "Some methods for classification and analysis of multivariate observations," in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967, pp. 281-297. 19. [19] C. Connolly and T. Fleiss, "A study of efficiency and accuracy in the transformation from RGB to CIELAB color space," IEEE Transactions on Image Processing, vol. 6, pp. 1046-1048, 1997. [ DOI:10.1109/83.597279] 20. [20] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "SLIC superpixels compared to state-of-the-art superpixel methods," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 34, pp. 2274-2282, 2012. [ DOI:10.1109/TPAMI.2012.120] 21. [21] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Kdd, 1996, pp. 226-231. 22. [22] B. Johnson and Z. Xie, "Unsupervised image segmentation evaluation and refinement using a multi-scale approach," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, pp. 473-483, 2011. [ DOI:10.1016/j.isprsjprs.2011.02.006] 23. [23] H. Zhang, J. E. Fritts, and S. A. Goldman, "Image segmentation evaluation: A survey of unsupervised methods," Computer Vision and Image Understanding, vol. 110, pp. 260-280, 2008. [ DOI:10.1016/j.cviu.2007.08.003] 24. [24] Y. J. Zhang, "Evaluation and comparison of different segmentation algorithms," Pattern recognition letters, vol. 18, pp. 963-974, 1997. [ DOI:10.1016/S0167-8655(97)00083-4] 25. [25] R. M. Haralick and L. G. Shapiro, "Image segmentation techniques," Computer vision, graphics, and image processing, vol. 29, pp. 100-132, 1985. [ DOI:10.1016/S0734-189X(85)90153-7] 26. [26] L. Yang, F. Albregtsen, T. Lønnestad, and P. Grøttum, "A supervised approach to the evaluation of image segmentation methods," in Computer Analysis of Images and Patterns, 1995, pp. 759-765. 27. [27] A. Lucieer and A. Stein, "Existential uncertainty of spatial objects segmented from satellite sensor imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 40, pp. 2518-2521, 2002. [ DOI:10.1109/TGRS.2002.805072] 28. [28] G. Espindola, G. Câmara, I. Reis, L. Bins, and A. Monteiro, "Parameter selection for region‐growing image segmentation algorithms using spatial autocorrelation," International Journal of Remote Sensing, vol. 27, pp. 3035-3040, 2006. [ DOI:10.1080/01431160600617194] 29. [29] T. Blaschke, G. J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, et al., "Geographic Object-Based Image Analysis–Towards a new paradigm," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 87, pp. 180-191, 2014. [ DOI:10.1016/j.isprsjprs.2013.09.014] 30. [30] o. r. w. senseFly. (2015). Available: https://www.sensefly.com/drones/example-datasets.html 31. [31] U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, "Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 58, pp. 239-258, 2004. [ DOI:10.1016/j.isprsjprs.2003.10.002] 32. [32] J. Im, L. J. Quackenbush, M. Li, and F. Fang, "Optimum Scale in Object‐Based Image Analysis," Scale Issues in Remote Sensing, pp. 197-214, 2014. [ DOI:10.1002/9781118801628.ch10] 33. [33] L. Drǎguţ, D. Tiede, and S. R. Levick, "ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data," International Journal of Geographical Information Science, vol. 24, pp. 859-871, 2010. [ DOI:10.1080/13658810903174803] |
