Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area

Geocarto International

Volumn 32, Issue 7, 2017, Pages 735-748

  Gibril, Mohamed Barakat A ^a   Bakar, Suzana A ^a   Yao, Kouame ^a   Idrees, Mohammed Oludare ^a   Pradhan, Biswajeet ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

image fusion; LULC; multisource data; Optical sensors; remote sensing; SAR

Indexed keywords

ACCURACY ASSESSMENT; AGRICULTURAL LAND; IMAGE CLASSIFICATION; LAND COVER; LANDSAT; MAPPING; MULTISPECTRAL IMAGE; RADARSAT; REMOTE SENSING; SATELLITE SENSOR; SYNTHETIC APERTURE RADAR; TRANSFORM;

MALAYSIA; PERAK; WEST MALAYSIA;

EID: 84963615019     PISSN: 10106049     EISSN: None     Source Type: Journal    
DOI: 10.1080/10106049.2016.1170893     Document Type: Article

References (47)

1. Adrian W, Storie CD, Storie J. 2014. Evaluating SAR-optical image fusions for urban LULC classification in Vancouver Canada. Can J Remote Sens. 40:278–290. doi:10.1080/07038992.2014.976700.
2. Al-Ahmadi FS, Hames AS. 2009. Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote akid areas, Kingdom of Saudi Arabia. Earth. 20:167–191.
3. Amarsaikhan D, Douglas T. 2004. Data fusion and multisource image classification. Int J Remote Sens. 25:3529–3539.10.1080/0143116031000115111
4. Amarsaikhan D, Saandar M., 2011. Fusion of multisource images for update of Urban GIS. In:Zheng Y, editor. Image fusion and its applications. Lorman (MS):Intech Open Access; p. 1–26. ISBN 978-953-307-182-4.
5. Amarsaikhan D, Ganzorig M, Ache P, Blotevogel HH. 2007. The integrated use of optical and InSAR data for urban land cover mapping. Int J Remote Sens. 28:1161–1171.10.1080/01431160600784267
6. Amarsaikhan D, Blotevogel H, Van Genderen J, Ganzorig M, Gantuya R, Nergui B. 2010. Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification. Int J Image Data Fusion. 1:83–97.10.1080/19479830903562041
7. Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm Remote Sens. 58:239–258.10.1016/j.isprsjprs.2003.10.002
8. Bhatta B. 2011. Remote sensing and GIS. New Delhi:Oxford University Press.
9. Byun Y, Choi J, Han Y. 2013. An area-based image fusion scheme for the integration of SAR and optical satellite imagery. IEEE J Sel Top Appl Earth Obs Remote Sens. 6:2212–2220.10.1109/JSTARS.2013.2272773
10. Congalton RG, Green K. 1993. A practical look at the sources of confusion in error matrix generation. Photogramm Eng Remote Sens. 59:641–644.
11. Dutra LV, Elmiro M, Freitas C, Santos JR, Mura JC, Filho B. 2002. The use of multi-frequency interferometric products to improve SAR imagery interpretability and classification by image fusion. In:Anais do III Workshop em Tratamento de Imagens, Junho, Belo Horizonte, Minas Gerais, Brazil. Available from:www.npdi.org
12. eCognition Developer. 2014. 9.0 User guide. Munich:Trimble Germany GmbH.
13. Ehlers M, Klonus S, Åstrand PJ. 2008. Quality assessment for multi-sensor multidate image fusion. CD-ROM Proceedings of ISPRS Congresses; 2008 July 3–11; Beijing, China.
14. Foody GM. 2002. Status of land cover classification accuracy assessment. Remote Sens Environ. 80:185–201.10.1016/S0034-4257(01)00295-4
15. Goetz S, Prince S, Small J. 2000. Advances in satellite remote sensing of environmental variables for epidemiological applications. Adv Parasitol. 47:289–307.10.1016/S0065-308X(00)47012-0
16. Hamedianfar A, Shafri HZM. 2014. Combining object-based classification and data mining algorithm to classify urban surface materials from WorldView-2 satellite image. 2014 International Conference on Information Science and Applications (ICISA); Seoul; p. 1–3.10.1109/ICISA.2014.6847378
17. Herold ND, Haack BN. 2002. Fusion of radar and optical data for land cover mapping. Geocarto Int. 17:21–30.10.1080/10106040208542232
18. Herold M, Roberts DA, Gardner ME, Dennison PE. 2004. Spectrometry for urban area remote sensing–development and analysis of a spectral library from 350 to 2400 nm. Remote Sens Environ. 91:304–319.10.1016/j.rse.2004.02.013
19. Huang S, Potter C, Crabtree CRL, Hager S, Gross P. 2010. Fusing optical and radar data to estimate sagebrush, herbaceous, and bare ground cover in yellowstone. Remote Sens Environ. 114:251–264.10.1016/j.rse.2009.09.013
20. Karathanassi V, Kolokousis P, Ioannidou S. 2007. A comparison study on fusion methods using evaluation indicators. Int J Remote Sens. 28:2309–2341.10.1080/01431160600606890
21. Kim M, Warner TA, Madden M, Atkinson DS. 2011. Multi-scale GEOBIA with very high spatial resolution digital aerial imagery:scale, texture and image objects. Int J Remote Sens. 32:2825–2850.10.1080/01431161003745608
22. Kruse F, Lefkoff A, Boardman J, Heidebrecht K, Shapiro A, Barloon P, Goetz A. 1993. The spectral image processing system (SIPS)–interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ. 44:145–163.10.1016/0034-4257(93)90013-N
23. Lee TS, Haque MA, Najim M. 2005. Scheduling the cropping calendar in wet-seeded rice schemes in Malaysia. Agric Water Manage. 71:71–84.10.1016/j.agwat.2004.06.007
24. Lu D, Batistella M, Moran E. 2007. Land‐cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data. Int J Remote Sens. 28:5447–5459.10.1080/01431160701227596
25. Marino CM, Panigada C, Busetto L. 2001. Airborne hyperspectral remote sensing applications in urban areas:asbestos concrete sheeting identification and mapping. Remote sensing and data fusion over urban areas, IEEE/ISPRS joint workshop. vol. 2001; Rome; p. 212–216.10.1109/DFUA.2001.985882
26. Mountrakis G, Im J, Ogole C. 2011. Support vector machines in remote sensing:a review. ISPRS J Photogramm Remote Sens. 66:247–259.10.1016/j.isprsjprs.2010.11.001
27. Neubert M, Herold H, Meinel G. 2008. Assessing image segmentation quality–concepts, methods and application. In:Blaschke T, Lang S, Hay GJ, editor. Object-based image analysis. Berlin:Springer; p. 769–784.
28. Nichol J, Wong MS. 2008. Habitat mapping in rugged terrain using multispectral IKONOS images. Photogramm Eng Remote Sens. 74:1325–1334.
29. Othman MT, Legarsky JJ, Davis CH. 2002. “Microwave and optical remote sensing study of Boone County, Missouri,” In:Geoscience and remote sensing symposium, 2002 IGARSS ‘02 2002 IEEE international; 2002 June 24–28; vol. 4; p. 2404–2406. doi:10.1109/IGARSS.2002.1026558.10.1109/IGARSS.2002.1026558
30. Pereira LDO, Freitas CDC, Sant′Anna SJS, Lu D, Moran EF. 2013. Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon. GIScience Remote Sens. 50:301–321.
31. Petropoulos GP, Vadrevu KP, Kalaitzidis C. 2013. Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region. Geocarto Int. 28:114–129.10.1080/10106049.2012.668950
32. Pohl C, Van Genderen JL. 1998. Review article multisensor image fusion in remote sensing:concepts, methods and applications. Int J Remote Sens. 19:823–854.10.1080/014311698215748
33. Ricchetti E. 2001. Visible-infrared and radar imagery fusion for geological application:a new approach using DEM and sun-illumination model. Int J Remote Sens. 22:2219–2230.
34. Rouse J, Jr., Haas R, Schell J, Deering D. 1974. Monitoring vegetation systems in the great plains with ERTS. NASA Spec Publ. 351:309–351.
35. Saadi NM, Watanabe K. 2009. Assessing image processing techniques for geological mapping:a case study in Eljufra, Libya. Geocarto Int. 24:241–253.10.1080/10106040802556199
36. Singh D, Maurya R, Shukla AS, Sharma MK, & Gupta P., 2012. Building extraction from very high resolution multispectral images using NDVI based segmentation and morphological operators. 2012 Students conference on engineering and systems (SCES); Allahabad; p. 1–5.
37. Smith A. 2010. Image segmentation scale parameter optimization and land cover classification using the random forest algorithm. J Spatial Sci. 55:69–79.10.1080/14498596.2010.487851
38. Strang G, Nguyen T. 1997. Wavelets and filter banks. Cambridge (MA):Wellesley-Cambridge Press; p. 490.
39. Teggi S, Cecchi R, Serafini F. 2003. TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the ‘a tròus’ algorithm. Int J Remote Sens. 24:1287–1301.10.1080/01431160210144561
40. Wang Y, Koopmans BN, Pohl C. 1995. Cover the 1995 flood in the Netherlands monitored from space–a multi-sensor approach. Int J Remote Sens. 16:2735–2739.10.1080/01431169508956399
41. Welch R, Ehlers W. 1987. Merging multi-resolution SPOT HRV nand Landsat TM data. Photogramm Eng Remote Sens. 53:301–303.
42. Westra T, Mertens KC, De Wulf RR. 2005. ENVISAT ASAR wide swath and SPOT‐vegetation image fusion for wetland mapping:evaluation of different wavelet‐based methods. Geocarto Int. 20:21–31.10.1080/10106040508542342
43. Wolf P. 2002. Elements of photogrammetry. 2nd ed. New York (NY):McGraw-Hill; p. 562.
44. Xu H. 2013. Rule-based impervious surface mapping using high spatial resolution imagery. Int J Remote Sens. 34:27–44.10.1080/01431161.2012.703343
45. Yoon G, Cho S, Chae G, Park J., 2005. Automatic land-cover classification of landsat images using feature database in a network. Proceedings of the IGARSS 2005 symposium, Seoul, Korea; p. 1–5. doi:10.1.1.131.6459.
46. Zhang J. 2010. Multi-source remote sensing data fusion:status and trends. Int J Image Data Fusion. 1:5–24.10.1080/19479830903561035
47. Zhang H, Zhang Yuanzhi, Lin Hui. 2012. Evaluation of the potential of ASAR data to estimate impervious surface area. Ann GIS. 18:289–298.10.1080/19475683.2012.730059