Spectral data treatments for impervious endmember derivation and fraction mapping from Landsat ETM+ imagery: a comparative analysis

Frontiers of Earth Science - Tập 9 Số 2 - Trang 179-191 - 2015
Wei Wang1, Xinfeng Yao2, Minbiao Ji1, Jiao Zhang1
1Key Laboratory of GIScience (Ministry of Education of China), East China Normal University, Shanghai, 200241, China
2Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai, 201403, China

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Tài liệu tham khảo

Arnold C L Jr, Gibbons C J (1996). Impervious surface coverage: the emergence of a key environmental indicator. J Am Plann Assoc, 62 (2): 243–258

Chavez P S, Sides S C, Anderson J A (1991). Comparison of three different methods to merge multiresolution and multispectral data-Landsat TM and SPOT panchromatic. Photogramm Eng Remote Sensing, 57(3): 295–303

Chen X, Li L (2008). A comparison of spectral mixture analysis methods for urban landscape using Landsat ETM+ data: Los Angeles, CA. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Beijing, China: 635–640

Foody G M (2002). Status of land cover classification accuracy assessment. Remote Sens Environ, 80(1): 185–201

González-Audícana M, Otazu X, Fors O, Seco A (2005). Comparison between Mallat’s and the’ à trous’ discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. Int J Remote Sens, 26(3): 595–614

González-Audícana M, Saleta J L, Catalán R G, García R (2004). Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing, 42(6): 1291–1299

Green A A, Berman M, Switzer P, Craig M D (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26(1): 65–74

Hu X, Weng Q (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multilayer perceptron neural networks. Remote Sens Environ, 113(10): 2089–2102

Im J, Lu Z, Rhee J, Quackenbush L J (2012). Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data. Remote Sens Environ, 117: 102–113

Ji M, Chen W, Wang W (2012). Improving spectral fidelity of WorldView-2 image fusion via a constrained generalized intensity-hue-saturation model with localized weight structure through land cover classification. J Appl Remote Sens, 6(1): 061707

Ji M, Feng J (2011). Subpixel measurement of mangrove canopy closure via spectral mixture analysis. Front Earth Sci, 5(2): 130–137

Ji M, Jensen J R (1999). Effectiveness of subpixel analysis in detecting and quantifying urban imperviousness from Landsat Thematic Mapper imagery. Geocarto Int, 14(4): 33–41

Jing L, Cheng Q (2011). An image fusion method for misaligned panchromatic and multispectral data. Int J Remote Sens, 32(4): 1125–1137

Li S, Kwok J T, Wang Y (2002). Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Inf Fusion, 3(1): 17–23

Lu D, Batistella M, Moran E, Mausel P (2004). Application of spectral mixture analysis to Amazonian land-use and land-cover classification. Int J Remote Sens, 25(23): 5345–5358

Lu D, Hetrick S, Moran E (2011). Impervious surface mapping with Quickbird imagery. Int J Remote Sens, 32(9): 2519–2533

Lu D, Moran E, Batistella M (2003). Linear mixture model applied to Amazonian vegetation classification. Remote Sens Environ, 87(4): 456–469

Lu D, Weng Q (2004). Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogramm Eng Remote Sensing, 70(9): 1053–1062

Lu D, Weng Q (2005). Urban classification using full spectral information of Landsat ETM+ imagery in Marion County, Indiana. Photogramm Eng Remote Sensing, 71(11): 1275–1284

Lu D, Weng Q (2006). Use of impervious surface in urban land-use classification. Remote Sens Environ, 102(1–2): 146–160

Mohapatra R P, Wu C (2008). Subpixel imperviousness estimation with IKONOS imagery: an artificial neural network approach. London: Taylor & Francis Group

Powell R L, Roberts D A, Dennison P E, Hess L L (2007). Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. Remote Sens Environ, 106(2): 253–267

Rashed T (2008). Remote sensing of within-class change in urban neighborhood structures. Comput Environ Urban Syst, 32(5): 343–354

Roberts D, Gardner M, Church R, Ustin S, Scheer G, Green R (1998). Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sens Environ, 65(3): 267–279

Smith M O, Johnson P E, Adams J B (1985). Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis. J Geophys Res, 90(S02): C797–C804

van de Voorde T, de Roeck T, Canters F (2009). A comparison of two spectral mixture modelling approaches for impervious surface mapping in urban areas. Int J Remote Sens, 30(18): 4785–4806

Wu C (2004). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sens Environ, 93(4): 480–492

Wu C (2009). Quantifying high-resolution impervious surfaces using spectral mixture analysis. Int J Remote Sens, 30(11): 2915–2932

Wu C, Murray A T (2003). Estimating impervious surface distribution by spectral mixture analysis. Remote Sens Environ, 84(4): 493–505

Xu H (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens, 27(14): 3025–3033

Yang B, Kim M, Madden M (2012). Assessing optimal image fusion methods for very high spatial resolution satellite images to support coastal monitoring. GIScience & Remote Sensing, 49(5): 687–710

Yuan F, Bauer M E (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ, 106(3): 375–386

Zurita-Milla R, Clevers J, Van Gijsel J, Schaepman M (2011). Using MERIS fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes. Int J Remote Sens, 32(4): 973–991