Built-Up Area Detection from Satellite Images Using Multikernel Learning, Multifield Integrating, and Multihypothesis Voting

IEEE Geoscience and Remote Sensing Letters

Volumn 12, Issue 6, 2015, Pages 1190-1194

  Li, Yansheng ^a   Tan, Yihua ^a   Li, Yi ^b   Qi, Shengxiang ^a   Tian, Jinwen ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b Ministry of Civil Affairs   (China)

Author keywords

Built up area detection; graph smoothing; multifield integrating; multihypothesis voting; multikernel learning

Indexed keywords

IMAGE ANALYSIS; IMAGE RECONSTRUCTION; PIXELS; REMOTE SENSING;

BUILT-UP AREA DETECTION; HIGH RESOLUTION REMOTE SENSING IMAGES; IMAGE INTERPRETATION; MULTI-HYPOTHESIS; MULTI-KERNEL LEARNING; MULTIPLE FEATURES; SATELLITE IMAGES; STATE-OF-THE-ART APPROACH; GRAPH SMOOTHING; MULTI-FIELD;

REMOTE SENSING; PIXELS;

EID: 84959343848     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2014.2387850     Document Type: Article

References (21)

1. M. Pesaresi, A. Gerhardinger, F. Kayitakire, "A robust built-up area presence index by anisotropic rotation-invariant textural measure," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 1, no. 3, pp. 180-192, Sep. 2008.
2. B. Sirmacek and C. Ünsalan, "Urban area detection using local feature points and spatial voting," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 1, pp. 146-150, Jan. 2010.
3. A. Kovacs and T. Sziranyi, "Improved Harris feature point set for orientation-sensitive urban-area detection in aerial images," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 4, pp. 796-800, Jul. 2013.
4. C. Tao, Y. Tan, Z.-R. Zou, J. Tian, "Unsupervised detection of builtup areas from multiple high-resolution remote sensing images," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 6, pp. 1300-1304, Nov. 2013.
5. G. Liu, G.-S. Xia, X. Huang, W. Yang, L. Zhang, "A perceptioninspired building index for automatic built-up area detection in highresolution satellite images," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2013, pp. 3132-3135.
6. X. Huang and L. Zhang, "Morphological building/shadow index for build extraction from high-resolution imagery over urban areas," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 1, pp. 161-172, Feb. 2012.
7. P. C. Smits and A. Annoni, "Updating land-cover maps by using texture information from very high-resolution space-borne imagery," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp. 1244-1254, May 1999.
8. S. Yu, M. Berthod, G. Giraudon, "Toward robust analysis of satellite images using map information-Application to urban area detection," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 4, pp. 1925-1939, Jul. 1999.
9. L. Weizman and J. Goldberger, "Urban-area segmentation using visual words," IEEE Geosci. Remote Sens. Lett., vol. 6, no. 3, pp. 388-392, Jul. 2009.
10. C. Ünsalan and K. L. Boyer, "Classifying land development in highresolution panchromatic satellite images using straight-line statistics," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 4, pp. 907-919, Apr. 2004.
11. C. Ünsalan and K. L. Boyer, "A theoretical and experimental investigation of graph theoretical measures for land development in satellite imagery," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 4, pp. 575-589, Apr. 2005.
12. P. Zhong and R. Wang, "Using combination of statistical models and multilevel structural information for detecting urban areas from a single gray-level image," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5, pp. 1469-1482, May 2007.
13. P. Zhong and R. Wang, "A multiple conditional random fields ensemble model for urban area detection in remote sensing optical images," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 3978-3988, Dec. 2007.
14. C. Tao, Y. Tan, J.-G. Yu, J. Tian, "Urban area detection using multiple kernel learning and graph cut," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2012, pp. 83-86.
15. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, Nov. 2004.
16. T. Leung and J. Malik, "Representing and recognizing the visual appearance of materials using 3-D textons," Int. J. Comput. Vis., vol. 43, no. 1, pp. 29-44, Jun. 2001.
17. A. Rakotomamonjy, F. Bach, S. Canu, Y. Grandvalet, "More efficiency in multiple kernel learning," in Proc. Int. Conf. Mach. Learn., 2007, pp. 775-782.
18. R. Achanta et al., "SLIC superpixels compared to state-of-the-art superpixels methods," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274-2282, Nov. 2012.
19. E. Erdem and A. Erdem, "Visual saliency estimation by non-linearity integrating features using region covariances," J. Vis., vol. 13, no. 4, pp. 1-20, Mar. 2013.
20. Ch. Yang, L. Zhang, H. Lu, "Graph-regularized saliency detection with convex-hull-based center prior," IEEE Signal Process. Lett., vol. 20, no. 7, pp. 637-640, Jul. 2013.
21. M. Pesaresi, D. Ehrlich, I. Caravaggi, M. Kauffmann, C. Louvrier, "Toward global automatic built-up are recognition using VHR imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 4, no. 4, pp. 923-934, Dec. 2011.