Target detection method for water mapping using landsat 8 OLI/TIRS Imagery

Water (Switzerland)

Volumn 7, Issue 2, 2015, Pages 794-817

  Ji, Luyan ^a   Geng, Xiurui ^b   Sun, Kang ^b   Zhao, Yongchao ^b   Gong, Peng ^a,c,d  

^a TSINGHUA UNIVERSITY   (China)

^b INSTITUTE OF ELECTRONICS   (China)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d JOINT CENTER FOR GLOBAL CHANGE STUDIES   (China)

Author keywords

CEM; Landsat 8 OLI TIRS; OWCEM (orthogonal subspace projection weighted CEM); Water extraction

Indexed keywords

COMPUTATIONAL ELECTROMAGNETICS; ICE; INFORMATION USE; LAKES; PIXELS; PROBABILITY DISTRIBUTIONS; REMOTE SENSING; SATELLITE IMAGERY; SPECTROSCOPY; SURFACE WATERS; WATER SUPPLY SYSTEMS;

AUTOCORRELATION MATRIX; CEM; CONSTRAINED ENERGY MINIMIZATION; HYPER-SPECTRAL IMAGERIES; LANDSAT; ORTHOGONAL SUBSPACE PROJECTION; RECEIVER OPERATING CHARACTERISTIC CURVES; WATER EXTRACTION;

EXTRACTION;

ACCURACY ASSESSMENT; ALGORITHM; LANDSAT; PROBABILITY; REMOTE SENSING; SATELLITE IMAGERY; SPECTRAL ANALYSIS; SURFACE WATER;

CHINA; GREAT LAKES [NORTH AMERICA]; HALA LAKE; LAKE HURON; QINGHAI;

PANDANUS TECTORIUS;

EID: 84925389156     PISSN: None     EISSN: 20734441     Source Type: Journal    
DOI: 10.3390/w7020794     Document Type: Article

References (42)

1. Roberts, N.; Taieb, M.; Barker, P.; Damnati, B.; Icole, M.; Williamson, D. Timing of the younger dryas event in east africa from lake-level changes. Nature 1993, 366, 146-148.
2. Gong, P. Remote sensing of environmental change over china: A review. Chin. Sci. Bull. 2012, 57, 2793-2801.
3. Feyisa, G.L.; Meilby, H.; Fensholt, R.; Proud, S.R. Automated water extraction index: A new technique for surface water mapping using landsat imagery. Remote Sens. Environ. 2014, 140, 23-35.
4. Sun, F.; Zhao, Y.; Gong, P.; Ma, R.; Dai, Y. Monitoring dynamic changes of global land cover types: Fluctuations of major lakes in china every 8 days during 2000-2010. Chin. Sci. Bull. 2014, 59, 171-189.
5. McIver, D.K.; Friedl, M.A. Using prior probabilities in decision-tree classification of remotely sensed data. Remote Sens. Environ. 2002, 81, 253-261.
6. Tulbure, M.G.; Broich, M. Spatiotemporal dynamic of surface water bodies using landsat time-series data from 1999 to 2011. ISPRS J. Photogramm. Remote Sens. 2013, 79, 44-52.
7. Sivanpillai, R.; Miller, S.N. Improvements in mapping water bodies using aster data. Ecol. Inform. 2010, 5, 73-78.
8. Sethre, P.R.; Rundquist, B.C.; Todhunter, P.E. Remote detection of prairie pothole ponds in the devils lake basin, north dakota. GISci. Remote Sens. 2005, 42, 277-296.
9. Work, E.A., Jr.; Gilmer, D.S. Utilization of satellite data for inventorying prairie ponds and lakes. Photogramm. Eng. Remote Sens. 1976, 42, 685-694.
10. Rundquist, D.C.; Lawson, M.P.; Queen, L.P.; Cerveny, R.S. The relationship between summer-season rainfall events and lake-surface area. J. Am. Water Resour. Assoc. 1987, 23, 493-508.
11. McFeeters, S.K. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425-1432.
12. Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025-3033.
13. Boland, D.H. Trophic Classification of Lakes Using Landsat-1 (erts-1) Multispectral Scanner Data; U.S. Environmental Protection Agency: Corvallis, OR, USA, 1976; p. 265.
14. Ji, L.; Zhang, L.; Wylie, B. Analysis of dynamic thresholds for the normalized difference water index. Photogramm. Eng. Remote Sens. 2009, 75, 1307-1317.
15. Rogers, A.S.; Kearney, M.S. Reducing signature variability in unmixing coastal marsh thematic mapper scenes using spectral indices. Int. J. Remote Sens. 2004, 25, 2317-2335.
16. Duan, Z.; Bastiaanssen, W.G.M. Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data. Remote Sens. Environ. 2013, 134, 403-416.
17. Poulin, B.; Davranche, A.; Lefebvre, G. Ecological assessment of phragmites australis wetlands using multi-season spot-5 scenes. Remote Sens. Environ. 2010, 114, 1602-1609.
18. Hui, F.; Xu, B.; Huang, H.; Yu, Q.; Gong, P. Modelling spatial-temporal change of poyang lake using multitemporal landsat imagery. Int. J. Remote Sens. 2008, 29, 5767-5784.
19. Chang, C.-I.; Althouse, M.L.G.; Sun, T.-L. Unsupervised interference rejection approach to target detection and classification for hyperspectral imagery. Opt. Eng. 1998, 37, 735-743.
20. Farrand, W.H.; Harsanyi, J.C. Mapping the distribution of mine tailings in the coeur d'alene river valley, idaho, through the use of a constrained energy minimization technique. Remote Sens. Environ. 1997, 59, 64-76.
21. Manolakis, D.; Shaw, G. Detection algorithms for hyperspectral imaging applications. IEEE Signal Proc. Mag. 2002, 19, 29-43.
22. Harsanyi, J.C. Detection and Classification of Subpixel Spectral Signatures in Hyperspectral Image Sequences; University of Maryland: College Park, MD, USA, 1993.
23. Du, Q.; Ren, H.; Chang, C.-I. A comparative study for orthogonal subspace projection and constrained energy minimization. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1525-1529.
24. Arora, M.K.; Bansal, S.; Khare, S.; Chauhan, K. Comparative assessment of some target detection algorithms for hyperspectral images. Def. Sci. J. 2013, 63, 53-62.
25. Manolakis, D.; Shaw, G.; Keshava, N. Comparative analysis of hyperspectral adaptive matched filter detector. Proc. SPIE 2000, doi:10.1117/12.410332.
26. Chen, J.Y.; Reed, I.S. A detection algorithm for optical targets in clutter. IEEE Trans. Aerosp. Electron. Syst. 1987, AES-23, 46-59.
27. Manolakis, D.; Lockwood, R.; Cooleyb, T.; Jacobson, J. Is there a best hyperspectral detection algorithm? Proc. SPIE 2009, doi:10.1117/12.816917.
28. Manolakis, D.; Lockwood, R.; Cooleyb, T.; Jacobson, J. Hyperspectral detection algorithms: Use covariances or subspaces? Proc. SPIE 2009, doi:10.1117/12.828397.
29. DiPietro, R.S.; Manolakis, D.; Lockwood, R.; Cooleyb, T.; Jacobson, J. Performance evaluation of hyperspectral detection algorithms for sub-pixel objects. Proc. SPIE 2010, doi:10.1117/12.850036.
30. Manolakis, D.; Marden, D.; Kerekes, J.; Shaw, G. On the statistics of hyperspectral imaging data. Proc. SPIE 2001, doi:10.1117/12.437021.
31. Funk, C.C.; Theiler, J.; Roberts, D.A.; Borel, C.C. Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery. IEEE Trans. Geosci. Remote Sens. 2001, 39, 1410-1420.
32. Minet, J.; Taboury, J.; Goudail, F.; Péalat, M.; Roux, N.; Lonnoy, J.; Ferrec, Y. Influence of band selection and target estimation error on the performance of the matched filter in hyperspectral imaging. Appl. Opt. 2011, 50, 4276-4284.
33. Ren, H.; Wang, C.-M.; Chang, C.-I.; Liu, J.-M.; Yang, C.-W.; Ma, D.-J.; Chieu, B.-C.; Lo, C.-S.; Chung, P.-C. Generalized constrained energy minimization approach to subpixel target detection for multispectral imagery. Opt. Eng. 2000, 39, 1275-1281.
34. Geng, X.; Ji, L.; Sun, K.; Zhao, Y. CEM: More bands, better performance. IEEE Geosci. Remote Sens. Lett. 2014, 11, 1-5.
35. Geng, X.; Zhao, Y. Principle of small target detection for hyperspectral imagery. Sci. China Earth Sci. 2007, 50, 1225-1231.
36. Wünnemann, B.; Wagner, J.; Zhang, Y.; Yan, D.; Wang, R.; Shen, Y.; Fang, X.; Zhang, J. Implications of diverse sedimentation patterns in hala lake, qinghai province, china for reconstructing late quaternary climate. J. Paleolimnol. 2012, 48, 725-749.
37. Gong, P.; Wang, J.; Yu, L.; Zhao, Y.; Zhao, Y.; Liang, L.; Niu, Z.; Huang, X.; Fu, H.; Liu, S.; et al. Finer resolution observation and monitoring of global land cover: First mapping results with landsat tm and etm+ data. Int. J. Remote Sens. 2012, 34, 2607-2654.
38. Chang, C.-I. An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis. IEEE Trans. Inf. Theory 2000, 46, 1927-1932.
39. Sun, F.; Sun, W.; Chen, J.; Gong, P. Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. Int. J. Remote Sens. 2012, 33, 6854-6875.
40. Geng, X.; Xiao, Z.; Ji, L.; Zhao, Y.; Wang, F. A gaussian elimination based fast endmember extraction algorithm for hyperspectral imagery. ISPRS J. Photogramm. Remote Sens. 2013, 79, 211-218.
41. Ji, L.; Geng, X.; Yu, K.; Zhao, Y. A new non-negative matrix factorization method based on barycentric coordinates for endmember extraction in hyperspectral remote sensing. Int. J. Remote Sens. 2013, 34, 6577-6586.
42. Acito, N.; Resta, S.; Diani, M.; Corsini, G. Robust technique for anomalous change detection in airborne hyperspectral imagery based on automatic and adaptive band selection. Opt. Eng. 2013, 52, doi:10.1117/1.OE.52.3.036202.