Mapping spectrally similar urban materials at sub-pixel scales

Alonzo, 2013, Identifying Santa Barbara's urban tree species from AVIRIS imagery using canonical discriminant analysis, Remote Sens. Lett., 4, 513, 10.1080/2150704X.2013.764027 Asner, 2003, Scale dependence of biophysical structure in deforested areas bordering the Tapajós National Forest, Central Amazon, Remote Sens. Environ., 87, 507, 10.1016/j.rse.2003.03.001 Bassani, 2007, Deterioration status of asbestos-cement roofing sheets assessed by analyzing hyperspectral data, Remote Sens. Environ., 109, 361, 10.1016/j.rse.2007.01.014 Bateson, 2000, Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis, IEEE Trans. Geosci. Remote Sens., 38, 1083, 10.1109/36.841987 Behling, 2015, Automated GIS-based derivation of urban ecological indicators using hyperspectral remote sensing and height information, Ecol. Indic., 48, 218, 10.1016/j.ecolind.2014.08.003 Berman, 2004, ICE: a statistical approach to identifying endmembers in hyperspectral images, IEEE Trans. Geosci. Remote Sens., 42, 2085, 10.1109/TGRS.2004.835299 Deng, 2016, Automated construction of multiple regional libraries for neighborhoodwise local multiple endmember unmixing, IEEE J. Sel. Topics Appl. Earth Obs. Remote. Sens., 9, 4232, 10.1109/JSTARS.2016.2541660 Dennison, 2004, A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper, Remote Sens. Environ., 93, 359, 10.1016/j.rse.2004.07.013 Dennison, 2003, Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE, Remote Sens. Environ., 87, 123, 10.1016/S0034-4257(03)00135-4 Dudley, 2015, A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients, Remote Sens. Environ., 167, 121, 10.1016/j.rse.2015.05.004 Ferrato, 2013, Comparing hyperspectral and multispectral imagery for land classification of the Lower Don River, Toronto, J. Geogr. Geol., 5, 92 Franke, 2009, Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments, Remote Sens. Environ., 113, 1712, 10.1016/j.rse.2009.03.018 Frolking, 2013, A global fingerprint of macro-scale changes in urban structure from 1999 to 2009, Environ. Res. Lett., 8, 10.1088/1748-9326/8/2/024004 Fu, 2016, A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery, Remote Sens. Environ., 175, 205, 10.1016/j.rse.2015.12.040 Goenaga, 2013, Unmixing analysis of a time series of hyperion images over the Guaica Dry Forest in Puerto Rico, IEEE J. Sel. Topics Appl. Earth Obs. Remote. Sens., 6, 329, 10.1109/JSTARS.2012.2225096 Green, 1998, Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS), Remote Sens. Environ., 65, 227, 10.1016/S0034-4257(98)00064-9 Grimmond, 2011, Initial results from phase 2 of the international urban energy balance model comparison, Int. J. Climatol., 31, 244, 10.1002/joc.2227 Gu, 2015, Detection of gradients of forest composition in an urban area using imaging spectroscopy, Remote Sens. Environ., 167, 168, 10.1016/j.rse.2015.06.010 Guanter, 2015, The EnMAP spaceborne imaging spectroscopy mission for earth observation, Remote Sens., 7, 8830, 10.3390/rs70708830 Hamlin, 2011, Imaging spectrometer science measurements for terrestrial ecology: AVIRIS and new developments, 3, 1 Heiden, 2012, Urban structure type characterization using hyperspectral remote sensing and height information, Landsc. Urban Plan., 105, 361, 10.1016/j.landurbplan.2012.01.001 Heiden, 2007, Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data, Remote Sens. Environ., 111, 537, 10.1016/j.rse.2007.04.008 Herold, 2004, Spectrometry for urban area remote sensing—development and analysis of a spectral library from 350 to 2400nm, Remote Sens. Environ., 91, 304, 10.1016/j.rse.2004.02.013 Herold, 2008, Imaging spectrometry and asphalt road surveys, Transp. Res. C: Emerg. Technol., 16, 153, 10.1016/j.trc.2007.07.001 Hodgson, 2003, Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness, Photogramm. Eng. Remote. Sens., 69, 973, 10.14358/PERS.69.9.973 Jensen, 2009 Jensen, 1999, Remote sensing of urban/suburban infrastructure and socio-economic attributes, Photogramm. Eng. Remote. Sens., 65, 611 Jensen, 2007, Remote sensing change detection in urban environments, 7 Kailath, 1967, The divergence and Bhattacharyya distance measures in signal selection, 15, 52 Labate, 2009, The PRISMA payload optomechanical design, a high performance instrument for a new hyperspectral mission, Acta Astronaut., 65, 1429, 10.1016/j.actaastro.2009.03.077 Lee, 2015, An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities, Remote Sens. Environ., 167, 6, 10.1016/j.rse.2015.06.012 Meister, 2000, BRDF field studies for remote sensing of urban areas, Remote Sens. Rev., 19, 37, 10.1080/02757250009532409 Myint, 2011, Per-pixel vs. object-based classification of urban land cover Extraction using high spatial resolution imagery, Remote Sens. Environ., 115, 1145, 10.1016/j.rse.2010.12.017 Myint, 2009, Modelling land-cover types using multiple endmember spectral mixture analysis in a Desert City, Int. J. Remote Sens., 30, 2237, 10.1080/01431160802549328 Myint, 2013, The impact of distinct anthropogenic and vegetation features on urban warming, Landsc. Ecol., 28, 959, 10.1007/s10980-013-9868-y Oke, 1987 Okujeni, 2015, Extending the vegetation–impervious–soil model using simulated EnMAP data and machine learning, Remote Sens. Environ., 158, 69, 10.1016/j.rse.2014.11.009 Okujeni, 2013, Support vector regression and synthetically mixed training data for quantifying urban land cover, Remote Sens. Environ., 137, 184, 10.1016/j.rse.2013.06.007 Ollinger, 2011, Sources of variability in canopy reflectance and the convergent properties of plants, New Phytol., 189, 375, 10.1111/j.1469-8137.2010.03536.x Peters, 2011, Seasonal contributions of vegetation types to suburban evapotranspiration, J. Geophys. Res. Biogeosci., 116 Plaza, 2004, A quantitative and comparative analysis of endmember Extraction algorithms from Hyperspectral data, IEEE Trans. Geosci. Remote Sens., 42, 650, 10.1109/TGRS.2003.820314 Powell, 2008, Characterizing variability of the urban physical environment for a suite of cities in Rondônia, Brazil, Earth Interact., 12, 1, 10.1175/2008EI246.1 Powell, 2007, Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil, Remote Sens. Environ., 106, 253, 10.1016/j.rse.2006.09.005 Ridd, 1995, Exploring a VIS (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities, Int. J. Remote Sens., 16, 2165, 10.1080/01431169508954549 Roberts, 2003, Evaluation of the potential of hyperion for fire danger assessment by comparison to the airborne visible/infrared imaging spectrometer, IEEE Trans. Geosci. Remote Sens., 41, 1297, 10.1109/TGRS.2003.812904 Roberts, 2015, Relationships between dominant plant species, fractional cover and land surface temperature in a Mediterranean ecosystem, Remote Sens. Environ., 167, 152, 10.1016/j.rse.2015.01.026 Roberts, 1998, Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models, Remote Sens. Environ., 65, 267, 10.1016/S0034-4257(98)00037-6 Roberts, 2012, Synergies between VSWIR and TIR data for the urban environment: an evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) decadal survey mission, Remote Sens. Environ., 117, 83, 10.1016/j.rse.2011.07.021 Roberts, 2004, Spectral and structural measures of northwest forest vegetation at leaf to landscape scales, Ecosystems, 7, 545, 10.1007/s10021-004-0144-5 Roessner, 2001, Automated differentiation of urban surfaces based on airborne hyperspectral imagery, IEEE Trans. Geosci. Remote Sens., 39, 1525, 10.1109/36.934082 Roth, 2012, Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data, Remote Sens. Environ., 127, 139, 10.1016/j.rse.2012.08.030 Schaaf, 2011, Mapping plant functional types at multiple spatial resolutions using imaging spectrometer data, GISci. Remote. Sens., 48, 324, 10.2747/1548-1603.48.3.324 Selva, 2015, Hyper-sharpening: a first approach on SIM-GA data, IEEE J. Sel. Topics Appl. Earth Obs. Remote. Sens., 8, 3008, 10.1109/JSTARS.2015.2440092 Settle, 1993, Linear mixing and the estimation of ground cover proportions, Int. J. Remote Sens., 14, 1159, 10.1080/01431169308904402 Small, 2001, Estimation of urban vegetation abundance by spectral mixture analysis, Int. J. Remote Sens., 22, 1305, 10.1080/01431160151144369 Somers, 2011, Endmember variability in spectral mixture analysis: a review, Remote Sens. Environ., 115, 1603, 10.1016/j.rse.2011.03.003 Somers, 2012, Automated extraction of image-based endmember bundles for improved spectral unmixing, IEEE J. Sel. Topics Appl. Earth Obs. Remote. Sens., 5, 396, 10.1109/JSTARS.2011.2181340 Song, 2004, Cross-sensor calibration between Ikonos and Landsat ETM+ for spectral mixture analysis, IEEE Geosci. Remote Sens. Lett., 1, 272, 10.1109/LGRS.2004.832227 Stein, 2003, Application of the normal compositional model to the analysis of hyperspectral imagery, 2003, 44 Thompson, 2015, Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign, Remote Sens. Environ., 167, 64, 10.1016/j.rse.2015.02.010 Weng, 2012, Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends, Remote Sens. Environ., 117, 34, 10.1016/j.rse.2011.02.030 Wu, 2004, Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery, Remote Sens. Environ., 93, 480, 10.1016/j.rse.2004.08.003 Wu, 2014, Spatially constrained multiple endmember spectral mixture analysis for quantifying subpixel urban impervious surfaces, IEEE J. Sel. Topics Appl. Earth Obs. Remote. Sens., 7, 1976, 10.1109/JSTARS.2014.2318018 Wu, 2003, Estimating impervious surface distribution by spectral mixture analysis, Remote Sens. Environ., 84, 493, 10.1016/S0034-4257(02)00136-0 Xiao, 2004, Using AVIRIS data and multiple-masking techniques to map urban forest tree species, Int. J. Remote Sens., 25, 5637, 10.1080/01431160412331291224 Zare, 2014, Endmember variability in hyperspectral analysis: addressing spectral variability during spectral unmixing, IEEE Signal Process. Mag., 31, 95, 10.1109/MSP.2013.2279177