Abstract
The study of urban area is one of the hottest research topics in the field of remote sensing. With the accumulation of high-resolution (HR) remote sensing data and emerging of new satellite sensors, HR observation of urban areas has become increasingly possible, which provides us with more elaborate urban information. However, the strong heterogeneity in the spectral and spatial domain of HR imagery brings great challenges to urban remote sensing. In recent years, numerous approaches were proposed to deal with HR image interpretation over complex urban scenes, including a series of features from low level to high level, as well as state-of-the-art methods depicting not only the urban extent, but also the intra-urban variations. In this paper, we aim to summarize the major advances in HR urban remote sensing from the aspects of feature representation and information extraction. Moreover, the future trends are discussed from the perspectives of methodology, urban structure and pattern characterization, big data challenge, and global mapping.
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References
Bechtel B, Alexander P, Böhner J, Ching J, Conrad O, Feddema J, Mills G, See L, Stewart I. 2015. Mapping local climate zones for a worldwide database of the form and function of cities. ISPRS Int Geo-Inf, 4: 199–219
Bechtel B, See L, Mills G, Foley M. 2016. Classification of local climate zones using SAR and multispectral data in an arid environment. IEEE J Sel Top Appl Earth Observ Remote Sens, 9: 3097–3105
Benediktsson J A, Palmason J A, Sveinsson J R. 2005. Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans Geosci Remote Sens, 43: 480–491
Benediktsson J A, Pesaresi M, Arnason K. 2003. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans Geosci Remote Sens, 41: 1940–1949
Bovolo F. 2009. A multilevel parcel-based approach to change detection in very high resolution multitemporal images. IEEE Geosci Remote Sens Lett, 6: 33–37
Bruzzone L, Bovolo F. 2012. A novel framework for the design of change-detection systems for very-high-resolution remote sensing images. Proc IEEE, 101: 609–630
Burkhard B, Kroll F, Nedkov S, Müller F. 2012. Mapping ecosystem service supply, demand and budgets. Ecol Indic, 21: 17–29
Chanussot J, Benediktsson J A, Fauvel M. 2006. Classification of remote sensing images from urban areas using a fuzzy possibilistic model. IEEE Geosci Remote Sens Lett, 3: 40–44
Chen J, Chen J, Liao A P, Cao X, Chen L J, Chen X H, He C Y, Han G, Peng S, Lu M, Zhang W W, Tong X H, Mills J. 2015. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J Photogramm Remote Sens, 103: 7–27
Chen X H, Cao X, Liao A P, Chen L J, Peng S, Lu M, Chen J, Zhang W W, Zhang H W, Han G, Wu H, Li R. 2016. Global mapping of artificial surfaces at 30-m resolution. Sci China Earth Sci, 59: 2295–2306
Chini M, Pierdicca N, Emery W J. 2009. Exploiting SAR and VHR optical images to quantify damage caused by the 2003 Bam earthquake. IEEE Trans Geosci Remote Sens, 47: 145–152
Dalla Mura M, Atli Benediktsson J, Waske B, Bruzzone L. 2010a. Extended profiles with morphological attribute filters for the analysis of hyperspectral data. Int J Remote Sens, 31: 5975–5991
Dalla Mura M, Benediktsson J A, Waske B, Bruzzone L. 2010b. Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans Geosci Remote Sens, 48: 3747–3762
Falco N, Mura M D, Bovolo F, Benediktsson J A, Bruzzone L. 2013. Change detection in VHR images based on morphological attribute profiles. IEEE Geosci Remote Sens Lett, 10: 636–640
Fauvel M, Benediktsson J A, Chanussot J, Sveinsson J R. 2008. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans Geosci Remote Sens, 46: 3804–3814
Florczyk A J, Ferri S, Syrris V, Kemper T, Halkia M, Soille P, Pesaresi M. 2016. A new European settlement map from optical remotely sensed data. IEEE J Sel Top Appl Earth Observ Remote Sens, 9: 1978–1992
Gamba P, Herold M. 2009. Global Mapping of Human Settlement: Experiences, Datasets, and Prospects. Boca Raton (FL): CRC Press. 374
Ghamisi P, Dalla Mura M, Benediktsson J A. 2015. A survey on spectral-spatial classification techniques based on attribute profiles. IEEE Trans Geosci Remote Sens, 53: 2335–2353
Gong P, Li X, Zhang W. 2019a. 40-year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Chin Sci Bull, 64: 756–763
Gong P, Liang S, Carlton E J, Jiang Q, Wu J, Wang L, Remais J V. 2012. Urbanisation and health in China. Lancet, 379: 843–852
Gong P, Liu H, Zhang M, Li C, Wang J, Huang H, Clinton N, Ji L, Li W, Bai Y, Chen B, Xu B, Zhu Z, Yuan C, Ping Suen H, Guo J, Xu N, Li W, Zhao Y, Yang J, Yu C, Wang X, Fu H, Yu L, Dronova I, Hui F, Cheng X, Shi X, Xiao F, Liu Q, Song L. 2019b. Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Chin Sci Bull, 64: 370–373
Gong P, Wang J, Yu L, Zhao Y, Zhao Y, Liang L, Niu Z, Huang X, Fu H, Liu S, Li C, Li X, Fu W, Liu C, Xu Y, Wang X, Cheng Q, Hu L, Yao W, Zhang H, Zhu P, Zhao Z, Zhang H, Zheng Y, Ji L, Zhang Y, Chen H, Yan A, Guo J, Yu L, Wang L, Liu X, Shi T, Zhu M, Chen Y, Yang G, Tang P, Xu B, Giri C, Clinton N, Zhu Z, Chen J, Chen J. 2013. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int J Remote Sens, 34: 2607–2654
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ, 202: 18–27
Guo X, Huang X, Zhang L. 2014. Three-dimensional wavelet texture feature extraction and classification for multi hyperspectral imagery. IEEE Geosci Remote Sens Lett, 11: 2183–2187
Haas J, Ban Y. 2017. Mapping and monitoring urban ecosystem services using multitemporal high-resolution satellite data. IEEE J Sel Top Appl Earth Observ Remote Sens, 10: 669–680
Haralick R M, Shanmugam K, Dinstein I H. 1973. Textural features for image classification. IEEE Trans Syst Man Cybern, SMC-3: 610–621
He C, Liu Z, Gou S, Zhang Q, Zhang J, Xu L. 2019. Detecting global urban expansion over the last three decades using a fully convolutional network. Environ Res Lett, 14: 034008
Hu F, Xia G S, Hu J, Zhang L. 2015. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens, 7: 14680–14707
Hu X, Weng Q. 2011. Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method. Geocarto Int, 26: 3–20
Huang B, Zhao B, Song Y. 2018. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sens Environ, 214: 73–86
Huang J K, Zhu L F, Deng X Z. 2007. Regional differences and determinants of built-up area expansion in China. Sci China Ser D-Earth Sci, 50: 1835–1843
Huang X, Chen H, Gong J. 2018. Angular difference feature extraction for urban scene classification using ZY-3 multi-angle high-resolution satellite imagery. ISPRS J Photogramm Remote Sens, 135: 127–141
Huang X, Guan X, Benediktsson J A, Zhang L, Li J, Plaza A, Dalla Mura M. 2014a. Multiple morphological profiles from multicomponent-base images for hyperspectral image classification. IEEE J Sel Top Appl Earth Observ Remote Sens, 7: 4653–4669
Huang X, Han X, Zhang L, Gong J, Liao W, Benediktsson J A. 2016. Generalized differential morphological profiles for remote sensing image classification. IEEE J Sel Top Appl Earth Observ Remote Sens, 9: 1736–1751
Huang X, Liu H, Zhang L. 2015a. Spatiotemporal detection and analysis of urban villages in mega city regions of China using high-resolution remotely sensed imagery. IEEE Trans Geosci Remote Sens, 53: 3639–3657
Huang X, Liu X, Zhang L. 2014b. A multichannel gray level co-occurrence matrix for multi/hyperspectral image texture representation. Remote Sens, 6: 8424–8445
Huang X, Wen D, Li J, Qin R. 2017a. Multi-level monitoring of subtle urban changes for the megacities of China using high-resolution multiview satellite imagery. Remote Sens Environ, 196: 56–75
Huang X, Xie C, Fang X, Zhang L. 2015b. Combining pixel- and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery. IEEE J Sel Top Appl Earth Observ Remote Sens, 8: 2097–2110
Huang X, Yuan W, Li J, Zhang L. 2017b. A new building extraction postprocessing framework for high-spatial-resolution remote-sensing imagery. IEEE J Sel Top Appl Earth Observ Remote Sens, 10: 654–668
Huang X, Zhang L. 2009. Road centreline extraction from high-resolution imagery based on multiscale structural features and support vector machines. Int J Remote Sens, 30: 1977–1987
Huang X, Zhang L. 2011. A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery. Photogramm Eng Remote Sens, 77: 721–732
Huang X, Zhang L. 2012a. Morphological building/shadow index for building extraction from high-resolution imagery over urban areas. IEEE J Sel Top Appl Earth Observ Remote Sens, 5: 161–172
Huang X, Zhang L. 2012b. A multiscale urban complexity index based on 3D wavelet transform for spectral-spatial feature extraction and classification: An evaluation on the 8-channel WorldView-2 imagery. Int J Remote Sens, 33: 2641–2656
Huang X, Zhang L, Li P. 2007a. An adaptive multiscale information fusion approach for feature extraction and classification of IKONOS multi-spectral imagery over urban areas. IEEE Geosci Remote Sens Lett, 4: 654–658
Huang X, Zhang L, Li P. 2007b. Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery. IEEE Geosci Remote Sens Lett, 4: 260–264
Kuang W H, Chen L J, Liu J Y, Xiang W N, Chi W F, Lu D S, Yang T R, Pan T, Liu A L. 2016. Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis. Sci China Earth Sci, 59: 1720–1737
Kuang W H, Yang T R, Liu A L, Zhang C, Lu D S, Chi W F. 2017. An EcoCity model for regulating urban land cover structure and thermal environment: Taking Beijing as an example. Sci China Earth Sci, 60: 1098–1109
Kumar A, Pandey A C, Jeyaseelan A T. 2012. Built-up and vegetation extraction and density mapping using WorldView-II. Geocarto Int, 27: 557–568
Li J, Huang X, Gong J. 2019. Deep neural network for remote-sensing image interpretation: Status and perspectives. Natl Sci Rev, doi:https://doi.org/10.1093/nsr/nwz058
Li Q, Huang X, Wen D, Liu H. 2017. Integrating multiple textural features for remote sensing image change detection. Photogramm Eng Remote Sens, 83: 109–121
Li S, Dragicevic S, Castro F A, Sester M, Winter S, Coltekin A, Pettit C, Jiang B, Haworth J, Stein A, Cheng T. 2016. Geospatial big data handling theory and methods: A review and research challenges. ISPRS J Photogramm Remote Sens, 115: 119–133
Li W, Chen C, Su H, Du Q. 2015. Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens, 53: 3681–3693
Li X, Gong P, Liang L. 2015. A 30-year (1984-2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sens Environ, 166: 78–90
Li X, Zhang C, Li W. 2017. Building block level urban land-use information retrieval based on Google Street View images. GISci Remote Sens, 54: 819–835
Li Y S, Huang X, Liu H. 2017. Unsupervised deep feature learning for urban village detection from high-resolution remote sensing images. Photogramm Eng Remote Sens, 83: 567–579
Liu C, Huang X, Wen D, Chen H, Gong J. 2017. Assessing the quality of building height extraction from ZiYuan-3 multi-view imagery. Remote Sens Lett, 8: 907–916
Liu C, Huang X, Zhu Z, Chen H, Tang X, Gong J. 2019. Automatic extraction of built-up area from ZY3 multi-view satellite imagery: Analysis of 45 global cities. Remote Sens Environ, 226: 51–73
Liu H, Huang X, Wen D, Li J. 2017. The use of landscape metrics and transfer learning to explore urban villages in China. Remote Sens, 9: 365
Liu X, Hu G, Chen Y, Li X, Xu X, Li S, Pei F, Wang S. 2018. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sens Environ, 209: 227–239
Longbotham N, Chaapel C, Bleiler L, Padwick C, Emery W J, Pacifici F. 2012. Very high resolution multiangle urban classification analysis. IEEE Trans Geosci Remote Sens, 50: 1155–1170
Ma Y, Wu H, Wang L, Huang B, Ranjan R, Zomaya A, Jie W. 2015. Remote sensing Big Data computing: Challenges and opportunities. Futur Gener Comp Syst, 51: 47–60
Mallat S G. 1989. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans Pattern Anal Mach Intell, 11: 674–693
Marin C, Bovolo F, Bruzzone L. 2015. Building change detection in multitemporal very high resolution SAR images. IEEE Trans Geosci Remote Sens, 53: 2664–2682
Marmanis D, Datcu M, Esch T, Stilla U. 2016. Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geosci Remote Sens Lett, 13: 105–109
Musci M, Feitosa R Q, Costa G A O P, Velloso M L F. 2013. Assessment of binary coding techniques for texture characterization in remote sensing imagery. IEEE Geosci Remote Sens Lett, 10: 1607–1611
Myint S W, Gober P, Brazel A, Grossman-Clarke S, Weng Q. 2011. Perpixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ, 115: 1145–1161
Myint S W, Lam N S N, Tyler J M. 2004. Wavelets for urban spatial feature discrimination. Photogramm Eng Remote Sens, 70: 803–812
Nogueira K, Penatti O A B, dos Santos J A. 2017. Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognit, 61: 539–556
Ojala T, Pietikäinen M, Harwood D. 1996. A comparative study of texture measures with classification based on featured distributions. Pattern Recognit, 29: 51–59
Ojala T, Pietikainen M, Maenpaa T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell, 24: 971–987
Ok A O. 2013. Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS J Photogramm Remote Sens, 86: 21–40
Ok A O, Senaras C, Yuksel B. 2013. Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery. IEEE Trans Geosci Remote Sens, 51: 1701–1717
Ouma Y O, Ngigi T G, Tateishi R. 2006. On the optimization and selection of wavelet texture for feature extraction from high-resolution satellite imagery with application towards urban-tree delineation. Int J Remote Sens, 27: 73–104
Pacifici F, Chini M, Emery W J. 2009. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification. Remote Sens Environ, 113: 1276–1292
Pacifici F, Del Frate F. 2010. Automatic change detection in very high resolution images with pulse-coupled neural networks. IEEE Geosci Remote Sens Lett, 7: 58–62
Peng F, Gong J, Wang L, Wu H, Liu P. 2017. A new stereo pair disparity index (SPDI) for detecting built-up areas from high-resolution stereo imagery. Remote Sens, 9: 633
Peng F, Wang L, Gong J, Wu H. 2015. Development of a framework for stereo image retrieval with both height and planar features. IEEE J Sel Top Appl Earth Observ Remote Sens, 8: 800–815
Pesaresi M, Benediktsson J A. 2001. A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans Geosci Remote Sens, 39: 309–320
Pesaresi M, Ehrilch D, Florczyk A, Freire S, Julea A, Kemper T, Soille P, Syrris V. 2015. GHS Built-up Grid, Derived from Landsat, Multitemporal (1975, 1990, 2000, 2014). European Commission, Joint Research Centre (JRC)
Pesaresi M, Ehrlich D, Caravaggi I, Kauffmann M, Louvrier C. 2011. Toward global automatic built-up area recognition using optical VHR imagery. IEEE J Sel Top Appl Earth Observ Remote Sens, 4: 923–934
Pesaresi M, Gerhardinger A. 2011. Improved textural built-up presence index for automatic recognition of human settlements in arid regions with scattered vegetation. IEEE J Sel Top Appl Earth Observ Remote Sens, 4: 16–26
Pesaresi M, Gerhardinger A, Kayitakire F Ç. 2008. A robust built-up area presence index by anisotropic rotation-invariant textural measure. IEEE J Sel Top Appl Earth Observ Remote Sens, 1: 180–192
Pesaresi M, Huadong G, Blaes X, Ehrlich D, Ferri S, Gueguen L, Halkia M, Kauffmann M, Kemper T, Lu L, Marin-Herrera M A, Ouzounis G K, Scavazzon M, Soille P, Syrris V, Zanchetta L. 2013. A global human settlement layer from optical HR/VHR RS data: Concept and first results. IEEE J Sel Top Appl Earth Observ Remote Sens, 6: 2102–2131
Poullis C. 2014. Tensor-Cuts: A simultaneous multi-type feature extractor and classifier and its application to road extraction from satellite images. ISPRS J Photogramm Remote Sens, 95: 93–108
Puissant A, Hirsch J, Weber C. 2005. The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery. Int J Remote Sens, 26: 733–745
Qian Y, Ye M, Zhou J. 2013. Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Trans Geosci Remote Sens, 51: 2276–2291
Qian Y, Zhou W, Yan J, Li W, Han L. 2015. Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery. Remote Sens, 7: 153–168
Qin R. 2014. Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery. ISPRS J Photogramm Remote Sens, 96: 179–192
Qin R, Fang W. 2014. A hierarchical building detection method for very high resolution remotely sensed images combined with DSM using graph cut optimization. Photogramm Eng Remote Sens, 80: 873–883
Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N, Prabhat N. 2019. Deep learning and process understanding for data-driven Earth system science. Nature, 566: 195–204
Schneider A, Friedl M A, Potere D. 2010. Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions’. Remote Sens Environ, 114: 1733–1746
Sghaier M O, Lepage R. 2016. Road extraction from very high resolution remote sensing optical images based on texture analysis and beamlet transform. IEEE J Sel Top Appl Earth Observ Remote Sens, 9: 1946–1958
Shanmugam L, Kaliaperumal V. 2015. Water flow based geometric active deformable model for road network. ISPRS J Photogramm Remote Sens, 102: 140–147
Shao Z, Fu H, Fu P, Yin L. 2016. Mapping urban impervious surface by fusing optical and SAR data at the decision level. Remote Sens, 8: 945–966
Shi X L, Nie S P, Ju W M, Yu L. 2016. Climate effects of the GlobeLand30 land cover dataset on the Beijing Climate Center climate model simulations. Sci China Earth Sci, 59: 1754–1764
Song C, Yang F, Li P. 2010. Rotation invariant texture measured by local binary pattern for remote sensing image classification. Wuhan: 2010 Second International Workshop on Education Technology and Computer Science. 3: 3–6
Stewart I D, Oke T R. 2012. Local climate zones for urban temperature studies. Bull Amer Meteorol Soc, 93: 1879–1900
Su W, Li J, Chen Y, Liu Z, Zhang J, Low T M, Suppiah I, Hashim S A M. 2008. Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery. Int J Remote Sens, 29: 3105–3117
Sun J, Zhang Y, Wu Z, Zhu Y, Yin X, Ding Z, Wei Z, Plaza J, Plaza A. 2019. An efficient and scalable framework for processing remotely sensed big data in cloud computing environments. IEEE Trans Geosci Remote Sens, 57: 4294–4308
Tian J, Chen D M. 2007. Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition. Int J Remote Sens, 28: 4625–4644
Tian J, Cui S, Reinartz P. 2014. Building change detection based on satellite stereo imagery and digital surface models. IEEE Trans Geosci Remote Sens, 52: 406–417
Tuia D, Pacifici F, Kanevski M, Emery W J. 2009. Classification of very high spatial resolution imagery using mathematical morphology and support vector machines. IEEE Trans Geosci Remote Sens, 47: 3866–3879
United Nations. 2018. 2018 Revision of World Urbanization Prospects. Population Division, Department of Economic and Social Affairs: United Nations Publications
Volpi M, Tuia D, Bovolo F, Kanevski M, Bruzzone L. 2013. Supervised change detection in VHR images using contextual information and support vector machines. Int J Appl Earth Observ Geoinf, 20: 77–85
Voltersen M, Berger C, Hese S, Schmullius C. 2014. Object-based land cover mapping and comprehensive feature calculation for an automated derivation of urban structure types at block level. Remote Sens Environ, 154: 192–201
Wang C, Middel A, Myint S W, Kaplan S, Brazel A J, Lukasczyk J. 2018. Assessing local climate zones in arid cities: The case of Phoenix, Arizona and Las Vegas, Nevada. ISPRS J Photogramm Remote Sens, 141: 59–71
Wen D, Huang X, Liu H, Liao W, Zhang L. 2017. Semantic classification of urban trees using very high resolution satellite imagery. IEEE J Sel Top Appl Earth Observ Remote Sens, 10: 1413–1424
Wen D, Huang X, Zhang L, Benediktsson J A. 2016. A novel automatic change detection method for urban high-resolution remotely sensed imagery based on multiindex scene representation. IEEE Trans Geosci Remote Sens, 54: 609–625
Weng Q. 2012. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens Environ, 117: 34–49
Xie C, Huang X, Zeng W, Fang X. 2016. A novel water index for urban high-resolution eight-band WorldView-2 imagery. Int J Digital Earth, 9: 925–941
Yoo H Y, Lee K, Kwon B D. 2009. Quantitative indices based on 3D discrete wavelet transform for urban complexity estimation using remotely sensed imagery. Int J Remote Sens, 30: 6219–6239
Yu X, Zhang B Q, Li Q, Chen J. 2016. A method characterizing urban expansion based on land cover map at 30 m resolution. Sci China Earth Sci, 59: 1738–1744
Zhang C, Sargent I, Pan X, Li H, Gardiner A, Hare J, Atkinson P M. 2019. Joint Deep Learning for land cover and land use classification. Remote Sens Environ, 221: 173–187
Zhang L, Huang X, Huang B, Li P. 2006. A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery. IEEE Trans Geosci Remote Sens, 44: 2950–2961
Zhang L, Zhang L, Tao D, Huang X. 2013. A modified stochastic neighbor embedding for multi-feature dimension reduction of remote sensing images. ISPRS J Photogramm Remote Sens, 83: 30–39
Zhang T, Huang X. 2018. Monitoring of urban impervious surfaces using time series of high-resolution remote sensing images in rapidly urbanized areas: A case study of Shenzhen. IEEE J Sel Top Appl Earth Observ Remote Sens, 11: 2692–2708
Zhang T, Huang X, Wen D, Li J. 2017. Urban building density estimation from high-resolution imagery using multiple features and support vector regression. IEEE J Sel Top Appl Earth Observ Remote Sens, 10: 3265–3280
Zhang X, Du S. 2015. A Linear Dirichlet Mixture Model for decomposing scenes: Application to analyzing urban functional zonings. Remote Sens Environ, 169: 37–49
Zhang Y, Zhang H, Lin H. 2014. Improving the impervious surface estimation with combined use of optical and SAR remote sensing images. Remote Sens Environ, 141: 155–167
Zhou P, Cheng G, Liu Z, Bu S, Hu X. 2016. Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping. Multidim Syst Sign Process, 27: 925–944
Zhu X X, Tuia D, Mou L, Xia G S, Zhang L, Xu F, Fraundorfer F. 2017. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geosci Remote Sens Mag, 5: 8–36
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 41771360 & 41842035), the National Program for Support of Top-notch Young Professionals, the Hubei Provincial Natural Science Foundation of China (Grant No. 2017CFA029), the National Key Research and Development Program of China (Grant No. 2016YFB0501403), and the Shenzhen Science and Technology Program (Grant No. JCYJ20180306170645080).
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Gong, J., Liu, C. & Huang, X. Advances in urban information extraction from high-resolution remote sensing imagery. Sci. China Earth Sci. 63, 463–475 (2020). https://doi.org/10.1007/s11430-019-9547-x
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DOI: https://doi.org/10.1007/s11430-019-9547-x