Thomas, N.[Nancy],
Hendrix, C.[Chad],
Congalton, R.G.[Russell G.],
A Comparison of Urban Mapping Methods Using High-Resolution Digital
Imagery,
PhEngRS(69), No. 9, September 2003, pp. 963-972.
An overview of a project in which one-meter digital imagery was used
to produce a map of pervious and impervious surfaces to be used by the
city of Scottsdale, Arizona for storm-water runoff estimation is
provided.
WWW Link.
0309
BibRef
Yang, L.M.[Li-Min],
Xian, G.[George],
Klaver, J.M.[Jacqueline M.],
Deal, B.[Brian],
Urban Land-Cover Change Detection through Sub-Pixel Imperviousness
Mapping Using Remotely Sensed Data,
PhEngRS(69), No. 9, September 2003, pp. 1003-1010.
An approach was developed to detect urban land-cover changes by
quantifying temporal change of an impervious surface
using Landsat and high-resolution imagery. Changes are at sub-pixel level.
WWW Link.
0309
BibRef
Hodgson, M.E.[Michael E.],
Jensen, J.R.[John R.],
Tullis, J.A.[Jason A.],
Riordan, K.D.[Kevin D.],
Archer, C.M.[Clark M.],
Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban
Parcel Imperviousness,
PhEngRS(69), No. 9, September 2003, pp. 973-980.
WWW Link.
0309
A comparative analysis of pixel-level (maximum-likelihood, ISODATA)
and segment-level classifiers is presented for
mapping urban parcel imperviousness from color aerial photography
fused with lidar-derived cover height.
BibRef
McMahon, G.[Gerard],
Consequences of Land-cover Misclassification in Models of Impervious
Surface,
PhEngRS(73), No. 12, December 2007, pp. 1343-1354.
WWW Link.
0712
An assessment of the effects of land-cover misclassification on models
that use the National Land Cover Data (NLCD) to predict impervious
surface by evaluating the consequences on model predictions of
adjusting land-cover within a watershed to reflect uncertainty
assessment information.
BibRef
Wu, C.S.[Chang-Shan],
Yuan, F.[Fei],
Seasonal Sensitivity Analysis of Impervious Surface Estimation with
Satellite Imagery,
PhEngRS(73), No. 12, December 2007, pp. 1393-1402.
WWW Link.
0712
A seasonal sensitivity analysis conducted for estimating impervious
surface distribution using spectral mixture analysis and regression
modeling.
BibRef
Yuan, F.[Fei],
Wu, C.S.[Chang-Shan],
Bauer, M.E.[Marvin E.],
Comparison of Spectral Analysis Techniques for Impervious Surface
Estimation Using Landsat Imagery,
PhEngRS(74), No. 8, August 2008, pp. 1045-1056.
WWW Link.
0804
Three common spectral analytical techniques (regression modeling,
regression tree, and normalized spectral mixture analysis) for
estimation of percent impervious surface area explored and compared in
terms of model accuracy, factors that influence model performance, and
cost of image processing.
BibRef
Weng, Q.,
Hu, X.,
Medium Spatial Resolution Satellite Imagery for Estimating and Mapping
Urban Impervious Surfaces Using LSMA and ANN,
GeoRS(46), No. 8, August 2008, pp. 2397-2406.
IEEE DOI
0808
BibRef
Zhou, Y.Y.[Yu-Yu],
Wang, Y.Q.,
Extraction of Impervious Surface Areas from High Spatial Resolution
Imagery by Multiple Agent Segmentation and Classification,
PhEngRS(74), No. 7, July 2008, pp. 857-868.
WWW Link.
0804
An algorithm of multiple agent segmentation and classification (MASC)
to extract impervious surface areas from truecolor digital orthophoto
and spatial resolution enhanced QuickBird-2 multispectral imagery.
BibRef
Luo, L.[Li],
Mountrakis, G.[Giorgos],
Converting local spectral and spatial information from a priori
classifiers into contextual knowledge for impervious surface
classification,
PandRS(66), No. 5, September 2011, pp. 579-587.
Elsevier DOI
1110
Contextual classification; Hybrid classifiers; Impervious surfaces;
Landsat ETM+; Partial classification
BibRef
Lu, D.S.[Deng-Sheng],
Li, G.Y.[Gui-Ying],
Moran, E.[Emilio],
Batistella, M.[Mateus],
Freitas, C.C.[Corina C.],
Mapping impervious surfaces with the integrated use of Landsat Thematic
Mapper and radar data: A case study in an urban-rural landscape in the
Brazilian Amazon,
PandRS(66), No. 6, November 2011, pp. 798-808.
Elsevier DOI
1112
Landsat TM; ALOS PALSAR L-band; RADARSAT-2 C-band; Wavelet-merging
technique; Spectral mixture analysis; Impervious surface
BibRef
Demarchi, L.[Luca],
Canters, F.[Frank],
Chan, J.C.W.[Jonathan Cheung-Wai],
van de Voorde, T.,
Multiple Endmember Unmixing of CHRIS/Proba Imagery for Mapping
Impervious Surfaces in Urban and Suburban Environments,
GeoRS(50), No. 9, September 2012, pp. 3409-3424.
IEEE DOI
1209
BibRef
Demarchi, L.[Luca],
Chan, J.C.W.[Jonathan Cheung-Wai],
Ma, J.L.[Jiang-Lin],
Canters, F.[Frank],
Mapping impervious surfaces from superresolution enhanced CHRIS/Proba
imagery using multiple endmember unmixing,
PandRS(72), No. 1, August 2012, pp. 99-112.
Elsevier DOI
1209
Angular hyperspectral imagery; CHRIS/Proba; Superresolution; Multiple
endmember unmixing; Impervious surface mapping; Urban areas
BibRef
Yang, F.[Fan],
Matsushita, B.[Bunkei],
Fukushima, T.[Takehiko],
Yang, W.[Wei],
Temporal mixture analysis for estimating impervious surface area from
multi-temporal MODIS NDVI data in Japan,
PandRS(72), No. 1, August 2012, pp. 90-98.
Elsevier DOI
1209
Impervious surface area; Temporal mixture analysis; MODIS; NDVI
time-series
BibRef
Parece, T.E.[Tammy E.],
Campbell, J.B.[James B.],
Comparing Urban Impervious Surface Identification Using Landsat and
High Resolution Aerial Photography,
RS(5), No. 10, 2013, pp. 4942-4960.
DOI Link
1311
BibRef
Deng, C.B.[Cheng-Bin],
Wu, C.S.[Chang-Shan],
The use of single-date MODIS imagery for estimating large-scale urban
impervious surface fraction with spectral mixture analysis and
machine learning techniques,
PandRS(86), No. 1, 2013, pp. 100-110.
Elsevier DOI
1312
Impervious surface
BibRef
Liu, X.P.[Xiao-Ping],
Hu, G.H.[Guo-Hua],
Ai, B.[Bin],
Li, X.[Xia],
Shi, Q.[Qian],
A Normalized Urban Areas Composite Index (NUACI) Based on Combination
of DMSP-OLS and MODIS for Mapping Impervious Surface Area,
RS(7), No. 12, 2015, pp. 15863.
DOI Link
1601
BibRef
Kaspersen, P.S.[Per Skougaard],
Fensholt, R.[Rasmus],
Drews, M.[Martin],
Using Landsat Vegetation Indices to Estimate Impervious Surface
Fractions for European Cities,
RS(7), No. 6, 2015, pp. 8224.
DOI Link
1507
BibRef
Deng, C.B.[Cheng-Bin],
Incorporating Endmember Variability into Linear Unmixing of Coarse
Resolution Imagery: Mapping Large-Scale Impervious Surface Abundance
Using a Hierarchically Object-Based Spectral Mixture Analysis,
RS(7), No. 7, 2015, pp. 9205.
DOI Link
1506
BibRef
Guo, W.[Wei],
Lu, D.S.[Deng-Sheng],
Wu, Y.[Yanlan],
Zhang, J.X.[Ji-Xian],
Mapping Impervious Surface Distribution with Integration of SNNP
VIIRS-DNB and MODIS NDVI Data,
RS(7), No. 9, 2015, pp. 12459.
DOI Link
1511
BibRef
Tsutsumida, N.[Narumasa],
Comber, A.J.[Alexis J.],
Barrett, K.[Kirsten],
Saizen, I.[Izuru],
Rustiadi, E.[Ernan],
Sub-Pixel Classification of MODIS EVI for Annual Mappings of
Impervious Surface Areas,
RS(8), No. 2, 2016, pp. 143.
DOI Link
1603
BibRef
Gallo, K.[Kevin],
Xian, G.[George],
Changes in satellite-derived impervious surface area at US historical
climatology network stations,
PandRS(120), No. 1, 2016, pp. 77-83.
Elsevier DOI
1610
Impervious surface area
BibRef
Zhang, L.[Lei],
Weng, Q.H.[Qi-Hao],
Annual Dynamics of Impervious Surface in the Pearl River Delta,
China, from 1988 to 2013, Using Time Series Landsat Imagery,
PandRS(113), No. 1, 2016, pp. 86-96.
Elsevier DOI
1602
Landsat
BibRef
Li, L.W.[Long-Wei],
Lu, D.S.[Deng-Sheng],
Kuang, W.H.[Wen-Hui],
Examining Urban Impervious Surface Distribution and Its Dynamic
Change in Hangzhou Metropolis,
RS(8), No. 3, 2016, pp. 265.
DOI Link
1604
BibRef
Lefebvre, A.[Antoine],
Sannier, C.[Christophe],
Corpetti, T.[Thomas],
Monitoring Urban Areas with Sentinel-2A Data: Application to the
Update of the Copernicus High Resolution Layer Imperviousness Degree,
RS(8), No. 7, 2016, pp. 606.
DOI Link
1608
BibRef
Shao, Z.F.[Zhen-Feng],
Fu, H.Y.[Hu-Yan],
Fu, P.[Peng],
Yin, L.[Li],
Mapping Urban Impervious Surface by Fusing Optical and SAR Data at
the Decision Level,
RS(8), No. 11, 2016, pp. 945.
DOI Link
1612
BibRef
Guo, W.[Wei],
Lu, D.S.[Deng-Sheng],
Kuang, W.H.[Wen-Hui],
Improving Fractional Impervious Surface Mapping Performance through
Combination of DMSP-OLS and MODIS NDVI Data,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link
1705
BibRef
Wang, P.[Panshi],
Huang, C.Q.[Cheng-Quan],
de Colstoun, E.C.B.[Eric C. Brown],
Mapping 2000-2010 Impervious Surface Change in India Using Global
Land Survey Landsat Data,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link
1705
BibRef
Silavi, T.[Tolue],
Hakimpour, F.[Farshad],
Claramunt, C.[Christophe],
Nourian, F.[Farshad],
The Legibility and Permeability of Cities: Examining the Role of
Spatial Data and Metrics,
IJGI(6), No. 4, 2017, pp. xx-yy.
DOI Link
1705
BibRef
Liu, S.[Shuai],
Gu, G.H.[Guang-Hua],
Improving the Impervious Surface Estimation from Hyperspectral Images
Using a Spectral-Spatial Feature Sparse Representation and
Post-Processing Approach,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Tang, F.[Fei],
Xu, H.Q.[Han-Qiu],
Impervious Surface Information Extraction Based on Hyperspectral
Remote Sensing Imagery,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Jia, Y.Q.[Yu-Qiu],
Tang, L.[Lina],
Wang, L.[Lin],
Influence of Ecological Factors on Estimation of Impervious Surface
Area Using Landsat 8 Imagery,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link
1708
BibRef
Sun, Z.C.[Zhong-Chang],
Wang, C.Z.[Cui-Zhen],
Guo, H.D.[Hua-Dong],
Shang, R.R.[Ran-Ran],
A Modified Normalized Difference Impervious Surface Index (MNDISI)
for Automatic Urban Mapping from Landsat Imagery,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link
1711
BibRef
Shi, L.F.[Ling-Fei],
Ling, F.[Feng],
Ge, Y.[Yong],
Foody, G.M.[Giles M.],
Li, X.D.[Xiao-Dong],
Wang, L.H.[Li-Hui],
Zhang, Y.H.[Yi-Hang],
Du, Y.[Yun],
Impervious Surface Change Mapping with an Uncertainty-Based
Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using
Landsat Time-Series Datasets from 1987 to 2016,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link
1712
See also Spatial-Temporal Super-Resolution Land Cover Mapping With a Local Spatial-Temporal Dependence Model.
BibRef
Zhang, X.[Xiya],
Li, P.J.[Pei-Jun],
A temperature and vegetation adjusted NTL urban index for urban area
mapping and analysis,
PandRS(135), No. Supplement C, 2018, pp. 93-111.
Elsevier DOI
1712
TVANUI, Urban extent, DMSP/OLS, NDVI, LST
BibRef
Zhang, J.[Jun],
Li, P.J.[Pei-Jun],
Mazher, A.[Abeer],
Liu, J.[Jing],
Impervious surface extraction with very high resolution imagery in
urban areas: Reducing tree obscuring effect,
CVRS12(94-99).
IEEE DOI
1302
BibRef
Chen, Y.H.[Yue-Hong],
Ge, Y.[Yong],
An, R.[Ru],
Chen, Y.[Yu],
Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed
Imagery with Points-of-Interest,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link
1804
See also Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery.
BibRef
Zhang, H.S.[Hong-Sheng],
Lin, H.[Hui],
Wang, Y.P.[Yun-Peng],
A new scheme for urban impervious surface classification from SAR
images,
PandRS(139), 2018, pp. 103-118.
Elsevier DOI
1804
SAR, Impervious surface, H/A/Alpha, VIS, Radsarsat-2
BibRef
Huang, M.[Min],
Chen, N.C.[Neng-Cheng],
Du, W.Y.[Wen-Ying],
Chen, Z.Q.[Ze-Qiang],
Gong, J.Y.[Jian-Ya],
DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach
by Detecting and Masking Background Land Cover on Google Earth Image,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link
1806
BibRef
Zhuo, L.[Li],
Shi, Q.L.[Qing-Li],
Tao, H.Y.[Hai-Yan],
Zheng, J.[Jing],
Li, Q.P.[Qiu-Ping],
An improved temporal mixture analysis unmixing method for estimating
impervious surface area based on MODIS and DMSP-OLS data,
PandRS(142), 2018, pp. 64-77.
Elsevier DOI
1807
Impervious surface area, Temporal mixture analysis,
Nonnegative matrix factorization,
DMSP-OLS
BibRef
Luo, H.[Hui],
Wang, L.[Le],
Wu, C.[Chen],
Zhang, L.[Lei],
An Improved Method for Impervious Surface Mapping Incorporating LiDAR
Data and High-Resolution Imagery at Different Acquisition Times,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link
1810
BibRef
Tian, Y.G.[Yu-Gang],
Chen, H.[Hui],
Song, Q.J.[Qing-Ju],
Zheng, K.[Kun],
A Novel Index for Impervious Surface Area Mapping:
Development and Validation,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link
1811
BibRef
Wickham, J.,
Herold, N.,
Stehman, S.V.,
Homer, C.G.,
Xian, G.,
Claggett, P.,
Accuracy assessment of NLCD 2011 impervious cover data for the
Chesapeake Bay region, USA,
PandRS(146), 2018, pp. 151-160.
Elsevier DOI
1812
MAUP, Mean Absolute Deviation (MAD), Mean Deviation (MD),
Regression, Water quality
BibRef
Huang, F.H.[Feng-Hua],
Yu, Y.[Ying],
Feng, T.H.[Ting-Hao],
Automatic extraction of impervious surfaces from high resolution
remote sensing images based on deep learning,
JVCIR(58), 2019, pp. 453-461.
Elsevier DOI
1901
High-resolution remote sensing images,
Impervious surfaces extraction, Bilateral filtering,
Improved watershed algorithm
BibRef
Huang, F.H.[Feng-Hua],
Yu, Y.[Ying],
Feng, T.H.[Ting-Hao],
Automatic extraction of urban impervious surfaces based on deep
learning and multi-source remote sensing data,
JVCIR(60), 2019, pp. 16-27.
Elsevier DOI
1903
Multi-source remote sensing data, Deep learning,
Extraction of urban impervious surface, ELM classifier,
Fuzzy C means clustering
BibRef
Huang, F.H.[Feng-Hua],
Yu, Y.[Ying],
Feng, T.H.[Ting-Hao],
Hyperspectral remote sensing image change detection based on tensor
and deep learning,
JVCIR(58), 2019, pp. 233-244.
Elsevier DOI
1901
Tensor model, Deep learning, Support tensor machine,
Hyperspectral remote sensing images, Change detection
BibRef
Huang, F.H.[Feng-Hua],
Yu, Y.[Ying],
Feng, T.H.[Ting-Hao],
Automatic building change image quality assessment in high resolution
remote sensing based on deep learning,
JVCIR(63), 2019, pp. 102585.
Elsevier DOI
1909
Deep belief network, Building change detection,
Morphological building index, Morphological shadow index,
Quality Assessment
BibRef
Wang, Y.L.[Yu-Liang],
Su, H.[Huiyi],
Li, M.[Mingshi],
An Improved Model Based Detection of Urban Impervious Surfaces Using
Multiple Features Extracted from ROSIS-3 Hyperspectral Images,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link
1902
BibRef
Fu, Y.Y.[Yong-Yong],
Liu, K.[Kunkun],
Shen, Z.Q.[Zhang-Quan],
Deng, J.S.[Jin-Song],
Gan, M.[Muye],
Liu, X.G.[Xin-Guo],
Lu, D.M.[Dong-Ming],
Wang, K.[Ke],
Mapping Impervious Surfaces in Town-Rural Transition Belts Using
China's GF-2 Imagery and Object-Based Deep CNNs,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link
1902
BibRef
Wang, B.B.[Bei-Bei],
Chen, Z.J.[Zhen-Jie],
Zhu, A.X.[A-Xing],
Hao, Y.Z.[Yu-Zhu],
Xu, C.Q.[Chang-Qing],
Multi-Level Classification Based on Trajectory Features of Time
Series for Monitoring Impervious Surface Expansions,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link
1903
BibRef
Kawakubo, F.[Fernando],
Morato, R.[Rúbia],
Martins, M.[Marcos],
Mataveli, G.[Guilherme],
Nepomuceno, P.[Pablo],
Martines, M.[Marcos],
Quantification and Analysis of Impervious Surface Area in the
Metropolitan Region of São Paulo, Brazil,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link
1905
BibRef
Xu, H.Y.[Hanze-Yu],
Wei, Y.C.[Yu-Chun],
Liu, C.[Chong],
Li, X.[Xiao],
Fang, H.[Hong],
A Scheme for the Long-Term Monitoring of Impervious-Relevant Land
Disturbances Using High Frequency Landsat Archives and the Google
Earth Engine,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link
1909
BibRef
Bian, J.H.[Jin-Hu],
Li, A.[Ainong],
Zuo, J.Q.[Jia-Qi],
Lei, G.B.[Guang-Bin],
Zhang, Z.J.[Zheng-Jian],
Nan, X.[Xi],
Estimating 2009-2017 Impervious Surface Change in Gwadar, Pakistan
Using the HJ-1A/B Constellation, GF-1/2 Data, and the Random Forest
Algorithm,
IJGI(8), No. 10, 2019, pp. xx-yy.
DOI Link
1910
BibRef
Xian, G.[George],
Shi, H.[Hua],
Anderson, C.[Cody],
Wu, Z.T.[Zhuo-Ting],
Assessment of the Impacts of Image Signal-to-Noise Ratios in
Impervious Surface Mapping,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link
1911
BibRef
Li, W.L.[Wen-Liang],
Mapping Urban Impervious Surfaces by Using Spectral Mixture Analysis
and Spectral Indices,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link
2001
BibRef
Li, W.L.[Wen-Liang],
Improving Urban Impervious Surfaces Mapping through Integrating
Statistical Methods and Spectral Mixture Analysis,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link
2107
BibRef
Zhang, H.[Hua],
Gorelick, S.M.[Steven M.],
Zimba, P.V.[Paul V.],
Extracting Impervious Surface from Aerial Imagery Using
Semi-Automatic Sampling and Spectral Stability,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link
2002
BibRef
Liang, Z.[Ze],
Wang, Y.Y.[Yue-Yao],
Sun, F.Y.[Fu-Yue],
Jiang, H.[Hong],
Huang, J.[Jiao],
Shen, J.S.[Jia-Shu],
Wei, F.[Feili],
Li, S.C.[Shuang-Cheng],
Exploring the Combined Effect of Urbanization and Climate Variability
on Urban Vegetation: A Multi-Perspective Study Based on More than
3000 Cities in China,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link
2004
BibRef
Zhao, J.[Jing],
Tsutsumida, N.[Narumasa],
Mapping Fragmented Impervious Surface Areas Overlooked by Global
Land-Cover Products in the Liping County, Guizhou Province, China,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link
2005
BibRef
Dong, X.G.[Xue-Gang],
Meng, Z.G.[Zhi-Guo],
Wang, Y.Z.[Yong-Zhi],
Zhang, Y.Z.[Yuan-Zhi],
Sun, H.T.[Hao-Teng],
Wang, Q.S.[Qing-Shuai],
Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing
City Using Random Forest Algorithm and Textural Features,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Zhang, L.[Lihao],
Tian, Y.G.[Yu-Gang],
Liu, Q.W.[Qing-Wei],
A Novel Urban Composition Index Based on Water-Impervious
Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping
Using Landsat Imagery,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Liao, W.[Wenyue],
Deng, Y.B.[Ying-Bin],
Li, M.[Miao],
Sun, M.[Meiwei],
Yang, J.[Ji],
Xu, J.H.[Jian-Hui],
Extraction and Analysis of Finer Impervious Surface Classes in Urban
Area,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
2102
BibRef
Tang, Y.[Yun],
Shao, Z.F.[Zhen-Feng],
Huang, X.[Xiao],
Cai, B.[Bowen],
Mapping Impervious Surface Areas Using Time-Series Nighttime Light
and MODIS Imagery,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Chen, R.[Rui],
Li, X.D.[Xiao-Dong],
Zhang, Y.H.[Yi-Hang],
Zhou, P.[Pu],
Wang, Y.[Yalan],
Shi, L.F.[Ling-Fei],
Jiang, L.[Lai],
Ling, F.[Feng],
Du, Y.[Yun],
Spatiotemporal Continuous Impervious Surface Mapping by Fusion of
Landsat Time Series Data and Google Earth Imagery,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Shrestha, B.[Binita],
Stephen, H.[Haroon],
Ahmad, S.[Sajjad],
Impervious Surfaces Mapping at City Scale by Fusion of Radar and
Optical Data through a Random Forest Classifier,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Parekh, J.R.[Jash R.],
Poortinga, A.[Ate],
Bhandari, B.[Biplov],
Mayer, T.[Timothy],
Saah, D.[David],
Chishtie, F.[Farrukh],
Automatic Detection of Impervious Surfaces from Remotely Sensed Data
Using Deep Learning,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Shen, J.Q.[Jia-Qi],
Shuai, Y.M.[Yan-Min],
Li, P.X.[Pei-Xian],
Cao, Y.X.[Yu-Xi],
Ma, X.W.[Xian-Wei],
Extraction and Spatio-Temporal Analysis of Impervious Surfaces over
Dongying Based on Landsat Data,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Cavalli, R.M.[Rosa Maria],
Capability of Remote Sensing Images to Distinguish the Urban Surface
Materials: A Case Study of Venice City,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Wang, S.S.[Shan-Shan],
Pu, Y.X.[Ying-Xia],
Li, S.F.[Sheng-Feng],
Li, R.[Runjie],
Li, M.[Maohua],
Spatio-Temporal Analysis of Impervious Surface Expansion in the
Qinhuai River Basin, China, 1988-2017,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Sun, G.[Genyun],
Cheng, J.[Ji],
Zhang, A.[Aizhu],
Jia, X.P.[Xiu-Ping],
Yao, Y.J.[Yan-Juan],
Jiao, Z.J.[Zhi-Jun],
Hierarchical fusion of optical and dual-polarized SAR on impervious
surface mapping at city scale,
PandRS(184), 2022, pp. 264-278.
Elsevier DOI
2202
Impervious surface, Fusion, Dual-polarized SAR, Shadow, Sentienl-1/2
BibRef
Ouyang, L.[Linke],
Wu, C.Y.[Cai-Yan],
Li, J.X.[Jun-Xiang],
Liu, Y.H.[Yu-Han],
Wang, M.[Meng],
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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Urban Areas, Change, Expansion and Growth .