Automatic newly increased built-up area extraction from high-resolution remote sensing images using line-density-based visual saliency and PanTex

Journal of Applied Remote Sensing

Volumn 12, Issue 1, 2018, Pages

  Wu, Tianjun ^a,b,c   Luo, Jiancheng ^d,e   Zhou, Xiaocheng ^b   Ma, Jianghong ^a   Song, Xueli ^a  

^a CHANG'AN UNIVERSITY   (China)

^b FUZHOU UNIVERSITY   (China)

^c Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province   (China)

^d INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^e UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Dempster Shafer evidence fusion; line density based visual saliency; newly increased built up areas; object based change detection; texture derived built up presence index (PanTex)

Indexed keywords

EXTRACTION; IMAGE SEGMENTATION; LAND USE; OBJECT RECOGNITION; SPECTRUM ANALYSIS; TEXTURES; VISUALIZATION;

BUILT-UP AREAS; DETECTION ACCURACY; EVIDENCE FUSION; HIGH RESOLUTION IMAGE; HIGH RESOLUTION REMOTE SENSING IMAGES; MULTI-TEMPORAL REMOTE SENSING; OBJECT BASED CHANGE DETECTIONS; VISUAL SALIENCY;

REMOTE SENSING;

EID: 85043517182     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.JRS.12.015016     Document Type: Article

References (35)

1. A. Karpatne et al., "Monitoring land-cover changes: a machine-learning perspective," IEEE Geosci. Remote Sens. Mag. 4(2), 8-21 (2016).
2. Y. Zhang et al., "Target-driven extraction of built-up land changes from high-resolution imagery," J. Appl. Remote Sens. 8(1), 084594 (2014).
3. M. C. Hansen and T. R. Loveland, "A review of large area monitoring of land cover change using Landsat data," Remote Sens. Environ. 122, 66-74 (2012).
4. X. P. Zhang et al., "Using long time series of Landsat data to monitor impervious surface dynamics: a case study in the Zhoushan Islands," J. Appl. Remote Sens. 7(7), 073515 (2013).
5. K. Green, D. Kempka, and L. Lackey, "Using remote sensing to detect and monitor landcover and land-use change," Photogramm. Eng. Remote Sens. 60(3), 331-337 (1994).
6. D. Lu et al., "Change detection techniques," Int. J. Remote Sens. 25(12), 2365-2401 (2004).
7. R. J. Radke et al., "Image change detection algorithms: a systematic survey," IEEE Trans. Image Process. 14(3), 294-307 (2005).
8. R. E. Kennedy et al., "Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects," Remote Sens. Environ. 113, 1382-1396 (2009).
9. T. Blaschke, "Object based image analysis for remote sensing," ISPRS J. Photogramm. Remote Sens. 65, 2-16 (2010).
10. J. Im, J. R. Jensen, and J. A. Tullis, "Object-based change detection using correlation image analysis and image segmentation," Int. J. Remote Sens. 29, 399-423 (2008).
11. L. Ping et al., "Object-oriented change detection for landslide rapid mapping," IEEE Geosci. Remote Sens. Lett. 8, 701-705 (2011).
12. G. Chen et al., "Object-based change detection," Int. J. Remote Sens. 33(14), 4434-4457 (2012).
13. M. Hussain et al., "Change detection from remotely sensed images: from pixel-based to object-based approaches," ISPRS J. Photogramm. Remote Sens. 80, 91-106 (2013).
14. G. Chen et al., "Object-based assessment of burn severity in diseased forests using highspatial and high-spectral resolution MASTER airborne imagery," ISPRS J. Photogramm. Remote Sens. 102, 38-47 (2015).
15. L. Bruzzone and D. F. Prieto, "An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote sensing images," IEEE Trans. Image Process. 11, 452-466 (2002).
16. A. Erener and H. S. B. Düzgün, "A methodology for land use change detection of high resolution pan images based on texture analysis," Ital. J. Remote Sens. 41(2), 47-59 (2009).
17. G. Mehmet et al., "Land cover classification and change-detection analysis using multitemporal remote sensed imagery and landscape metrics," Eur. J. Remote Sens. 45(1), 1-18 (2012).
18. Q. H. Weng, "Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends," Remote Sens. Environ. 117, 34-49 (2012).
19. C. L. Arnold, Jr. and C. J. Gibbons, "Impervious surface coverage: the emergence of a key environmental indicator," J. Am. Plann. Association 62(2), 243-258 (1996).
20. H. G. Akcay and S. Aksoy, "Building detection using directional spatial constraints," in Int. Geoscience and Remote Sensing Symp. (IGARSS 2010), pp. 1932-1935 (2010).
21. X. Y. Yu et al., "Impervious surface extraction using coupled spectral-spatial features," J. Appl. Remote Sens. 10(3), 035013 (2016).
22. H. Q. Xu, "Extraction of urban built-up land features from Landsat imagery using a thematic-oriented index combination technique," Photogramm. Eng. Remote Sens. 73(12), 1381-1391 (2007).
23. P. Gong and P. Howarth, "The use of structural information for improving land-cover classification accuracies at the rural-urban fringe," ISPRS J. Photogramm. Remote Sens. 56(1), 67-73 (1990).
24. M. Pesaresi, "Texture analysis for urban pattern recognition using fine-resolution panchromatic satellite imagery," Geogr. Environ. Modell. 4, 43-63 (2000).
25. M. Pesaresi, A. Gerhardinger, and F. Kayitakire, "A robust built-up area presence index by anisotropic rotation-invariant textural measure," IEEE J-STARS 1, 180-192 (2008).
26. M. Pesaresi, "Improved textural built-up presence index for automatic recognition of human settlements in arid regions with scattered vegetation," IEEE J-STARS 4, 16-26 (2011).
27. L. P. Zhang et al., "A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery," IEEE Trans. Geosci. Remote Sens. 44(10), 2950-2961 (2006).
28. X. Huang and L. P. Zhang, "Morphological building/shadow index for building extraction from high-resolution imagery over urban areas," IEEE J-STARS 5(1), 161-172 (2012).
29. P. L. Rosin, "A simple method for detecting salient regions," Pattern Recognit. 42(11), 2363-2371 (2009).
30. D. Comaniciu and P. Meer, "Mean shift: a robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603-619 (2002).
31. I. Ruthven and M. Lalmas, "Using Dempster-Shafer's theory of evidence to combine aspects of information use," J. Intell. Inf. Syst. 19(3), 267-301 (2003).
32. W. Deng et al. "Discussion of remote sensing image classification method based on evidence theory," J. Remote Sens. 11(4), 568-573 (2007).
33. Q. Zhu, S. Xu, and L. Han, "A new shadow extraction method from color aerial images based on Dempster-Shafer evidence theory," Acta Autom. Sin. 33(6), 588-595 (2007).
34. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Syst., Man, Cybern. 9(1), 62-66 (1979).
35. D. L. Olson and D. Delen, Advanced Data Mining Techniques, Springer, Berlin, Heidelberg (2008).