Classification for Urban Area Land Cover, Remote Sensing

Chapter Contents (Back)
Classification. Remote Sensing. Urban Area. See also Urban Heat Islands, Surface Temperature, Remote Sensing. See also General Urban Area Detection, Change and Growth.

Wharton, S.W.[Stephen W.],
A Contextual Classification Method for Recognizing Land Use Patterns in High Resolution Remotely Sensed Data,
PR(15), No. 4, 1982, pp. 317-324.
Elsevier DOI BibRef 8200

Heikkonen, J., Varfis, A.,
Land Cover Land Use Classification of Urban Areas: A Remote-Sensing Approach,
PRAI(12), No. 4, June 1998, pp. 475-489. 9808

Heikkonen, J.[Jukka], Varfis, A.[Aristide], and Kanellopoulos, I.[Ioannis],
A Method for Remote Sensing Based Classification of Urban Areas,
HTML Version. 9705

Chan, J.C.W.[Jonathan Cheung-Wai], Chan, K.P.[Kwok-Ping], Yeh, A.G.O.[Anthony Gar-On],
Detecting the Nature of Change in an Urban Environment: A Comparison of Machine Learning Algorithms,
PhEngRS(67), No. 2, February 2001, pp. 213-226. The same procedure of land-cover change detection was implemented using four different machine learning algorithms, and those algorithms were compared based on recognition rates, ease of use, and degree of automation. 0102

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

Islam, Z., Metternicht, G.,
The Performance of Fuzzy Operators on Fuzzy Classification of Urban Land Covers,
PhEngRS(71), No. 1, January 2005, pp. 59-68. Evaluation of the performance of fuzzy operators for integrating fuzzy membership values associated with multiple spectral bands for mapping urban land covers.
WWW Link. 0509

Pozzi, F.[Francesca], Small, C.[Christopher],
Analysis of Urban Land Cover and Population Density in the United States,
PhEngRS(71), No. 6, June 2005, pp. 719-726. Analysis of population density and vegetation distribution for several cities shows a strong correspondence in cities with high population density but considerable regional variability that precludes simple spectral classifications of land cover.
WWW Link. 0509

Nemmour, H.[Hassiba], Chibani, Y.[Youcef],
Multiple support vector machines for land cover change detection: An application for mapping urban extensions,
PandRS(61), No. 2, November 2006, pp. 125-133.
Elsevier DOI 0703
Change detection; Fuzzy Integral; Combination; Support vector machines; Attractor dynamics BibRef

Huang, X.[Xin], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang],
Classification of Very High Spatial Resolution Imagery Based on the Fusion of Edge and Multispectral Information,
PhEngRS(74), No. 12, December 2008, pp. 1585-1597.
WWW Link. 0804
A new algorithm to classify high spatial resolution remotely sensed imagery by integrating fuzzy edge information and multispectral features. BibRef

Huang, X.[Xin], Zhang, L.P.[Liang-Pei],
An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery,
GeoRS(46), No. 12, December 2008, pp. 4173-4185.

Huang, X.[Xin], Zhang, L.P.[Liang-Pei],
An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery,
GeoRS(51), No. 1, January 2013, pp. 257-272.

Zhao, Y.D.[Yin-Di], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang], Huang, B.[Bo],
Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features,
GeoRS(45), No. 5, May 2007, pp. 1458-1468.

Zhao, B.[Bei], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei], Huang, B.[Bo],
The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification,
RS(8), No. 2, 2016, pp. 157.
DOI Link 1603

Zhong, Y.F.[Yan-Fei], Zhao, B.[Bei], Zhang, L.P.[Liang-Pei],
Multiagent Object-Based Classifier for High Spatial Resolution Imagery,
GeoRS(52), No. 2, February 2014, pp. 841-857.
evolutionary computation BibRef

Zhong, Y.F.[Yan-Fei], Zhao, J., Zhang, L.P.[Liang-Pei],
A Hybrid Object-Oriented Conditional Random Field Classification Framework for High Spatial Resolution Remote Sensing Imagery,
GeoRS(52), No. 11, November 2014, pp. 7023-7037.
Context modeling BibRef

Zhao, J.[Ji], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
Detail-Preserving Smoothing Classifier Based on Conditional Random Fields for High Spatial Resolution Remote Sensing Imagery,
GeoRS(53), No. 5, May 2015, pp. 2440-2452.
geophysical image processing BibRef

Zhao, J.[Ji], Zhong, Y.F.[Yan-Fei], Shu, H., Zhang, L.P.[Liang-Pei],
High-Resolution Image Classification Integrating Spectral-Spatial-Location Cues by Conditional Random Fields,
IP(25), No. 9, September 2016, pp. 4033-4045.
geophysical image processing BibRef

Zhang, L.P.[Liang-Pei], Zhao, Y.D.[Yin-Di], Huang, B.[Bo], Li, P.X.[Ping-Xiang],
Texture Feature Fusion with Neighborhood-Oscillating Tabu Search for High Resolution Image Classification,
PhEngRS(74), No. 3, March 2008, pp. 323-332.
WWW Link. 0803
Neighborhood-Oscillating tabu search integrates different types of texture features to improve classifi cation performance of high-resolution imagery. BibRef

Wu, S.S.[Shuo-Sheng], Xu, B.[Bing], Wang, L.[Le],
Urban Land-use Classification Using Variogram-based Analysis with an Aerial Photograph,
PhEngRS(72), No. 7, July 2006, pp. 813-822.
WWW Link. 0610
A variogram-based texture analysis was tested for classifying detailed urban land-use classes, such as mobile home, singlefamily house, multi-family house, industrial, and commercial, from a digital color infrared aerial photograph. BibRef

van de Voorde, T.[Tim], de Genst, W.[William], Canters, F.[Frank],
Improving Pixel-based VHR Land-cover Classifications of Urban Areas with Post-classification Techniques,
PhEngRS(73), No. 9, September 2007, pp. 1017-1028.
WWW Link. 0709
Three post-classification techniques were applied to improve the accuracy and the structural coherence of an urban land-cover map derived from a soft pixel-based classification. BibRef

Bellens, R., Gautama, S., Martinez-Fonte, L., Philips, W., Chan, J.C.W., Canters, F.[Frank],
Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles,
GeoRS(46), No. 10, October 2008, pp. 2803-2813.

Chan, J.C.W.[Jonathan Cheung-Wai], Bellens, R.[Rik], Canters, F.[Frank], Gautama, S.[Sidharta],
An Assessment of Geometric Activity Features for Per-pixel Classification of Urban Man-made Objects using Very High Resolution Satellite Imagery,
PhEngRS(75), No. 4, April 2009, pp. 397-412.
WWW Link. 0903
The results of using geometric activity features based on ridge-based modeling and morphological profi les for the classification of urban man-made objects from an Ikonos image. BibRef

Xu, B.[Bing], Gong, P.[Peng],
Land-use/Land-cover Classification with Multispectral and Hyperspectral EO-1 Data,
PhEngRS(73), No. 8, August 2007, pp. 955-965.
WWW Link. 0709
Land-use and land-cover classification in an urban rural fringe of the San Francisco Bay Area using EO-1 Hyperion imagery is compared with that using EO-1 ALI imagery, and the application of a computationally efficient segmentation-based feature reduction approach. BibRef

Myint, S.W.[Soe W.], Wentz, E.A.[Elizabeth A.], Purkis, S.J.[Sam J.],
Employing Spatial Metrics in Urban Land-use/Landcover Mapping: Comparing the Getis and Geary Indices,
PhEngRS(73), No. 12, December 2007, pp. 1403-1417.
WWW Link. 0712
The effectiveness of Getis index (Gi) in comparison to a measure of spatial autocorrelation (Geary's C) in classifying landuse / land-cover classes in a high resolution imagery and the impact of distance threshold used in Getis index with regards to the classification accuracy. BibRef

Huang, H.[Heng], Legarsky, J.[Justin], Othman, M.[Maslina],
Land-cover Classification Using Radarsat and Landsat Imagery for St. Louis, Missouri,
PhEngRS(73), No. 1, January 2007, pp. 37-44.
WWW Link. 0704
An investigation of the classification accuracy of merging satellite imagery from Radarsat and Landsat missions. BibRef

Walton, J.T.[Jeffrey T.],
Subpixel Urban Land Cover Estimation: Comparing Cubist, Random Forests, and Support Vector Regression,
PhEngRS(74), No. 10, October 2008, pp. 1213-1222.
WWW Link. 0804
Three machine learning subpixel estimation methods were applied to estimate urban cover and the resulting predictions were compared based on accuracy. BibRef

Aytekin, Ö.[Örsan], Ulusoy, I.[Ilkay],
Automatic segmentation of VHR images using type information of local structures acquired by mathematical morphology,
PRL(32), No. 13, 1 October 2011, pp. 1618-1625.
Elsevier DOI 1109
Image segmentation; Differential morphological profile (DMP); Very high resolution (VHR) images; Mathematical morphology Morphology to get scale. BibRef

Miyazaki, H., Iwao, K., Shibasaki, R.,
Development of a New Ground Truth Database for Global Urban Area Mapping from a Gazetteer,
RS(3), No. 6, June 2011, pp. 1177-1187.
DOI Link 1203

d'Oleire-Oltmanns, S., Coenradie, B., Kleinschmit, B.,
An Object-Based Classification Approach for Mapping Migrant Housing in the Mega-Urban Area of the Pearl River Delta (China),
RS(3), No. 8, August 2011, pp. 1710-1723.
DOI Link 1203

Matikainen, L., Karila, K.,
Segment-Based Land Cover Mapping of a Suburban Area: Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points,
RS(3), No. 8, August 2011, pp. 1777-1804.
DOI Link 1203

Moskal, L., Styers, D., Halabisky, M.,
Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data,
RS(3), No. 10, October 2011, pp. 2243-2262.
DOI Link 1203

Novack, T., Esch, T., Kux, H., Stilla, U.,
Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification,
RS(3), No. 10, October 2011, pp. 2263-2282.
DOI Link 1203

Hartfield, K., Landau, K., Leeuwen, W.,
Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat,
RS(3), No. 11, November 2011, pp. 2364-2383.
DOI Link 1203

Hofmann, P., Strobl, J., Nazarkulova, A.,
Mapping Green Spaces in Bishkek: How Reliable can Spatial Analysis Be?,
RS(3), No. 6, June 2011, pp. 1088-1103.
DOI Link 1203

Longbotham, N., Chaapel, C., Bleiler, L., Padwick, C., Emery, W.J., Pacifici, F.,
Very High Resolution Multiangle Urban Classification Analysis,
GeoRS(50), No. 4, April 2012, pp. 1155-1170.

Salehi, B., Zhang, Y., Zhong, M., Dey, V.,
Object-Based Classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data,
RS(4), No. 8, August 2012, pp. 2256-2276.
DOI Link 1209

Soheili Majd, M.[Maryam], Simonetto, E.[Elisabeth], Polidori, L.[Laurent],
Maximum Likelihood Classification of Single Highresolution Polarimetric SAR Images in Urban Areas,
PFG(2012), No. 4, 2012, pp. 395-407.
WWW Link. 1211
Maximum Likelihood Classification of High-Resolution Polarimetric SAR Images in Urban Area,
PDF File. 1106

Ogashawara, I., Bastos, V.,
A Quantitative Approach for Analyzing the Relationship between Urban Heat Islands and Land Cover,
RS(4), No. 11, November 2012, pp. 3596-3618.
DOI Link 1211

Nichol, J.E.[Janet E.], To, P.H.[Pui Hang],
Temporal characteristics of thermal satellite images for urban heat stress and heat island mapping,
PandRS(74), No. 1, November 2012, pp. 153-162.
Elsevier DOI 1212
Thermal; Satellite; Urban; Climate BibRef

Singh, K.K.[Kunwar K.], Vogler, J.B.[John B.], Shoemaker, D.A.[Douglas A.], Meentemeyer, R.K.[Ross K.],
LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy,
PandRS(74), No. 1, November 2012, pp. 110-121.
Elsevier DOI 1212
LiDAR; Landsat; Fusion; Land cover; Large-area assessment; Mapping accuracy; Managed clearings BibRef

Pan, G., Qi, G., Wu, Z., Zhang, D., Li, S.,
Land-Use Classification Using Taxi GPS Traces,
ITS(14), No. 1, March 2013, pp. 113-123.

Ban, Y., Jacob, A.,
Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping,
GeoRS(51), No. 4, April 2013, pp. 1998-2006.

Shen, L.[Luou], Lu, C.[Chenxi], Zhao, F.[Fang], Liu, W.M.[Wei-Ming],
Discrete Fourier Transformation for Seasonal-Factor Pattern Classification and Assignment,
ITS(14), No. 2, 2013, pp. 511-516.
DFT; land use characteristic; urban area; Roads BibRef

Johnson, B.[Brian], Xie, Z.X.[Zhi-Xiao],
Classifying a high resolution image of an urban area using super-object information,
PandRS(83), No. 1, 2013, pp. 40-49.
Elsevier DOI 1308
Segmentation BibRef

Kohli, D.[Divyani], Warwadekar, P.[Pankaj], Kerle, N.[Norman], Sliuzas, R.[Richard], Stein, A.[Alfred],
Transferability of Object-Oriented Image Analysis Methods for Slum Identification,
RS(5), No. 9, 2013, pp. 4209-4228.
DOI Link 1310

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

Wu, H.[Hao], Sun, Y.[Yurong], Shi, W.Z.[Wen-Zhong], Chen, X.L.[Xiao-Ling], Fu, D.J.[Dong-Jie],
Examining the Satellite-Detected Urban Land Use Spatial Patterns Using Multidimensional Fractal Dimension Indices,
RS(5), No. 10, 2013, pp. 5152-5172.
DOI Link 1311

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

Xu, S.[Sudan], Vosselman, G.[George], Elberink, S.O.[Sander Oude],
Multiple-entity based classification of airborne laser scanning data in urban areas,
PandRS(88), No. 1, 2014, pp. 1-15.
Elsevier DOI 1402
Airborne laser scanning BibRef

Xu, S.[Sudan], Vosselman, G.[George], Elberink, S.O.[Sander Oude],
Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data,
RS(7), No. 12, 2015, pp. 15867.
DOI Link 1601
See also Detection of Curbstones in Airborne Laser Scanning Data. BibRef

Vosselman, G.,
Point cloud segmentation for urban scene classification,
DOI Link 1402

Belgiu, M.[Mariana], Dragut, L.[Lucian], Strobl, J.[Josef],
Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery,
PandRS(87), No. 1, 2014, pp. 205-215.
Elsevier DOI 1402
Land Cover BibRef

Meganem, I., Deliot, P., Briottet, X., Deville, Y., Hosseini, S.,
Linear-Quadratic Mixing Model for Reflectances in Urban Environments,
GeoRS(52), No. 1, January 2014, pp. 544-558.
geophysical image processing BibRef

Li, C.C.[Cong-Cong], Wang, J.[Jie], Wang, L.[Lei], Hu, L.Y.[Luan-Yun], Gong, P.[Peng],
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery,
RS(6), No. 2, 2014, pp. 964-983.
DOI Link 1403

Carlei, V.[Vittorio], Nuccio, M.[Massimiliano],
Mapping industrial patterns in spatial agglomeration: A SOM approach to Italian industrial districts,
PRL(40), No. 1, 2014, pp. 1-10.
Elsevier DOI 1403
Self-organizing maps BibRef

Huang, X.[Xin], Lu, Q.[Qikai], Zhang, L.P.[Liang-Pei],
A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas,
PandRS(90), No. 1, 2014, pp. 36-48.
Elsevier DOI 1404
High spatial resolution BibRef

Wieland, M.[Marc], Pittore, M.[Massimiliano],
Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images,
RS(6), No. 4, 2014, pp. 2912-2939.
DOI Link 1405

Zhou, W.Q.[Wei-Qi], Cadenasso, M.L.[Mary. L.], Schwarz, K.[Kirsten], Pickett, S.T.A.[Steward T.A.],
Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification,
RS(6), No. 4, 2014, pp. 3369-3386.
DOI Link 1405

Kotthaus, S.[Simone], Smith, T.E.L.[Thomas E.L.], Wooster, M.J.[Martin J.], Grimmond, C.S.B.,
Derivation of an urban materials spectral library through emittance and reflectance spectroscopy,
PandRS(94), No. 1, 2014, pp. 194-212.
Elsevier DOI 1407
Spectral library BibRef

Okujeni, A.[Akpona], van der Linden, S.[Sebastian], Jakimow, B.[Benjamin], Rabe, A.[Andreas], Verrelst, J.[Jochem], Hostert, P.[Patrick],
A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover,
RS(6), No. 7, 2014, pp. 6324-6346.
DOI Link 1408

Galletti, C.S.[Christopher S.], Myint, S.W.[Soe W.],
Land-Use Mapping in a Mixed Urban-Agricultural Arid Landscape Using Object-Based Image Analysis: A Case Study from Maricopa, Arizona,
RS(6), No. 7, 2014, pp. 6089-6110.
DOI Link 1408

Haberman, D.[Daniel], Gillies, L.[Laura], Canter, A.[Aryeh], Rinner, V.[Valentine], Pancrazi, L.[Laetitia], Martellozzo, F.[Federico],
The Potential of Urban Agriculture in Montréal: A Quantitative Assessment,
IJGI(3), No. 3, 2014, pp. 1101-1117.
DOI Link 1410

Du, P.J.[Pei-Jun], Liu, P.[Pei], Xia, J.[Junshi], Feng, L.[Li], Liu, S.[Sicong], Tan, K.[Kun], Cheng, L.[Liang],
Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System and Examples,
RS(6), No. 10, 2014, pp. 9458-9474.
DOI Link 1411

Rahman, M.M.[Mir Mustafizur], Hay, G.J.[Geoffrey J.], Couloigner, I.[Isabelle], Hemachandran, B.[Bharanidharan],
Transforming Image-Objects into Multiscale Fields: A GEOBIA Approach to Mitigate Urban Microclimatic Variability within H-Res Thermal Infrared Airborne Flight-Lines,
RS(6), No. 10, 2014, pp. 9435-9457.
DOI Link 1411

O'Neil-Dunne, J.[Jarlath], MacFaden, S.[Sean], Royar, A.[Anna],
A Versatile, Production-Oriented Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes Using GEOBIA and Data Fusion,
RS(6), No. 12, 2014, pp. 12837-12865.
DOI Link 1412

Rau, J.Y.[Jiann-Yeou], Jhan, J.P.[Jyun-Ping], Hsu, Y.C.[Ya-Ching],
Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment,
GeoRS(53), No. 3, March 2015, pp. 1304-1319.
feature extraction BibRef

Ðuric, N.[Nataša], Pehani, P.[Peter], Oštir, K.[Krištof],
Application of In-Segment Multiple Sampling in Object-Based Classification,
RS(6), No. 12, 2014, pp. 12138-12165.
DOI Link 1412
Urban area. BibRef

Feng, Q.L.[Quan-Long], Liu, J.T.[Jian-Tao], Gong, J.H.[Jian-Hua],
UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis,
RS(7), No. 1, 2015, pp. 1074-1094.
DOI Link 1502
See also Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier: The Case of Yuyao, China. BibRef

Li, M.M.[Meng-Meng], Bijker, W.[Wietske], Stein, A.[Alfred],
Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover,
PandRS(102), No. 1, 2015, pp. 48-61.
Elsevier DOI 1503
Urban land cover BibRef

Chhetri, S.K.[Sachin Kumar], Kayastha, P.[Prabin],
Manifestation of an Analytic Hierarchy Process (AHP) Model on Fire Potential Zonation Mapping in Kathmandu Metropolitan City, Nepal,
IJGI(4), No. 1, 2015, pp. 400-417.
DOI Link 1504

Su, G.W.[Gui-Wu], Qi, W.H.[Wen-Hua], Zhang, S.L.[Su-Ling], Sim, T.[Timothy], Liu, X.S.[Xin-Sheng], Sun, R.[Rui], Sun, L.[Lei], Jin, Y.F.[Yi-Fan],
An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China,
RS(7), No. 3, 2015, pp. 2543-2601.
DOI Link 1504

Blaschke, T.[Thomas], Hay, G.J.[Geoffrey J.], Weng, Q.[Qihao], Resch, B.[Bernd],
Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems: An Overview,
RS(3), No. 8, August 2011, pp. 1743-1776.
DOI Link Award, Remote Sensing, Review, Second. 2015. BibRef 1108

Yang, J.X.[Jin-Xin], Wong, M.S.[Man Sing], Menenti, M.[Massimo], Nichol, J.[Janet],
Modeling the effective emissivity of the urban canopy using sky view factor,
PandRS(105), No. 1, 2015, pp. 211-219.
Elsevier DOI 1506
Urban geometry BibRef

Yang, J.X.[Jin-Xin], Wong, M.S.[Man Sing], Menenti, M.[Massimo], Nichol, J.[Janet],
Study of the geometry effect on land surface temperature retrieval in urban environment,
PandRS(109), No. 1, 2015, pp. 77-87.
Elsevier DOI 1512
Urban surface temperature BibRef

Cheng, G.[Gong], Han, J.W.[Jun-Wei], Guo, L.[Lei], Liu, Z.B.[Zhen-Bao], Bu, S.H.[Shu-Hui], Ren, J.C.[Jin-Chang],
Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images,
GeoRS(53), No. 8, August 2015, pp. 4238-4249.
land use 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

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

Matasci, G.[Giona], Longbotham, N.[Nathan], Pacifici, F.[Fabio], Kanevski, M.[Mikhail], Tuia, D.[Devis],
Understanding angular effects in VHR imagery and their significance for urban land-cover model portability: A study of two multi-angle in-track image sequences,
PandRS(107), No. 1, 2015, pp. 99-111.
Elsevier DOI 1508
Image classification BibRef

Wu, W.[Wenjin], Guo, H.[Huadong], Li, X.[Xinwu], Ferro-Famil, L., Zhang, L.[Lu],
Urban Land Use Information Extraction Using the Ultrahigh-Resolution Chinese Airborne SAR Imagery,
GeoRS(53), No. 10, October 2015, pp. 5583-5599.
Gaussian distribution 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

Calegari, G.R.[Gloria Re], Carlino, E.[Emanuela], Peroni, D.[Diego], Celino, I.[Irene],
Extracting Urban Land Use from Linked Open Geospatial Data,
IJGI(4), No. 4, 2015, pp. 2109.
DOI Link 1511

Zhang, Q.[Qian], Qin, R.J.[Rong-Jun], Huang, X.[Xin], Fang, Y.[Yong], Liu, L.[Liang],
Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile,
RS(7), No. 12, 2015, pp. 15840.
DOI Link 1601
Dealing with high resolution for classification. BibRef

Yan, Y.[Yan], Zhang, C.[Chi], Hu, Y.[Yunfeng], Kuang, W.H.[Wen-Hui],
Urban Land-Cover Change and Its Impact on the Ecosystem Carbon Storage in a Dryland City,
RS(8), No. 1, 2016, pp. 6.
DOI Link 1602

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

Comber, A.J.[Alexis J.], Harris, P.[Paul], Tsutsumida, N.[Narumasa],
Improving land cover classification using input variables derived from a geographically weighted principal components analysis,
PandRS(119), No. 1, 2016, pp. 347-360.
Elsevier DOI 1610
GWmodel BibRef

Momeni, R.[Rahman], Aplin, P.[Paul], Boyd, D.S.[Doreen S.],
Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach,
RS(8), No. 2, 2016, pp. 88.
DOI Link 1603

Ma, P., Lin, H.,
Robust Detection of Single and Double Persistent Scatterers in Urban Built Environments,
GeoRS(54), No. 4, April 2016, pp. 2124-2139.
Interferometry BibRef

Yang, Y.[Yetao], Wang, Y.[Yi], Wu, K.[Ke], Yu, X.[Xin],
Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City,
RS(8), No. 4, 2016, pp. 304.
DOI Link 1604

Xiang, D.L.[De-Liang], Tang, T.[Tao], Ban, Y.F.[Yi-Fang], Su, Y.[Yi], Kuang, G.Y.[Gang-Yao],
Unsupervised Polarimetric SAR Urban Area Classification Based on Model-Based Decomposition with Cross Scattering,
PandRS(116), No. 1, 2016, pp. 86-100.
Elsevier DOI 1604
Cross scattering matrix See also Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence. BibRef

Li, X.[Xueke], Wu, T.[Taixia], Liu, K.[Kai], Li, Y.[Yao], Zhang, L.[Lifu],
Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification,
RS(8), No. 5, 2016, pp. 438.
DOI Link 1606

Karalas, K.[Konstantinos], Tsagkatakis, G.[Grigorios], Zervakis, M.[Michael], Tsakalides, P.[Panagiotis],
Land Classification Using Remotely Sensed Data: Going Multilabel,
GeoRS(54), No. 6, June 2016, pp. 3548-3563.
feature extraction BibRef

Kim, Y.M.[Yong-Min],
Generation of Land Cover Maps through the Fusion of Aerial Images and Airborne LiDAR Data in Urban Areas,
RS(8), No. 6, 2016, pp. 521.
DOI Link 1608

Deilami, K.[Kaveh], Kamruzzaman, M., Hayes, J.F.[John Francis],
Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia,
RS(8), No. 9, 2016, pp. 716.
DOI Link 1610

Zhang, Q.[Qi], Xin, J.[Jinyuan], Yin, Y.[Yan], Wang, L.[Lili], Wang, Y.[Yuesi],
The Variations and Trends of MODIS C5 & C6 Products' Errors in the Recent Decade over the Background and Urban Areas of North China,
RS(8), No. 9, 2016, pp. 754.
DOI Link 1610

Alqurashi, A.F.[Abdullah F.], Kumar, L.[Lalit], Sinha, P.[Priyakant],
Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia,
RS(8), No. 10, 2016, pp. 838.
DOI Link 1609

Zheng, X.Y.[Xin-Yu], Wang, Y.[Yang], Gan, M.[Muye], Zhang, J.[Jing], Teng, L.M.[Long-Mei], Wang, K.[Ke], Shen, Z.Q.[Zhang-Quan], Zhang, L.[Ling],
Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region,
RS(8), No. 10, 2016, pp. 845.
DOI Link 1609

Li, M.M.[Meng-Meng], Stein, A.[Alfred], Bijker, W.[Wietske], Zhan, Q.M.[Qing-Ming],
Urban land use extraction from Very High Resolution remote sensing imagery using a Bayesian network,
PandRS(122), No. 1, 2016, pp. 192-205.
Elsevier DOI 1612
Urban land use BibRef

Schreyer, J.[Johannes], Lakes, T.[Tobia],
Deriving and Evaluating City-Wide Vegetation Heights from a TanDEM-X DEM,
RS(8), No. 11, 2016, pp. 940.
DOI Link 1612

Gervais, N.[Norman], Buyantuev, A.[Alexander], Gao, F.[Feng],
Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702

Kampffmeyer, M.[Michael], Salberg, A.B.[Arnt-Børre], Jenssen, R.[Robert],
Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks,

Zeng, Y., Huang, W., Jin, W., Li, S.,
Multi-agent Based Simulation Of Optimal Urban Land Use Allocation In The Middle Reaches Of The Yangtze River, China,
ISPRS16(B8: 1089-1092).
DOI Link 1610

Willkomm, M., Dannenberg, P.,
Monitoring Land Use Dynamics Of Peri-urban Agricultutre In Central Kenya With Rapideye Satellite Imagery,
ISPRS16(B8: 1079-1081).
DOI Link 1610

Li, F.[Feng], Han, L.[Liu], Liujun, Z.[Zhu], Yinyou, H.[Huang], Song, G.[Guo],
Urban Vegetation Mapping Based On The Hj-1 Ndvi Reconstrction,
ISPRS16(B8: 867-871).
DOI Link 1610

Roychowdhury, K.,
Comparison Between Spectral, Spatial And Polarimetric Classification Of Urban And Periurban Landcover Using Temporal Sentinel: 1 Images,
ISPRS16(B7: 789-796).
DOI Link 1610
Ths 4: Tandem-x BibRef

Zhang, Y., Qin, K., Zeng, C., Zhang, E.B., Yue, M.X., Tong, X.,
A Data Field Method For Urban Remotely Sensed Imagery Classification Considering Spatial Correlation,
ISPRS16(B7: 431-435).
DOI Link 1610

Yao, W., Poleswki, P., Krzystek, P.,
Classification Of Urban Aerial Data Based On Pixel Labelling With Deep Convolutional Neural Networks And Logistic Regression,
ISPRS16(B7: 405-410).
DOI Link 1610

Manzke, N.[Nina], Kada, M.[Martin], Kastler, T.[Thomas], Xu, S.[Shaojuan], de Lange, N.[Norbert], Ehlers, M.[Manfred],
The Urbis Project: Identification And Characterization Of Potential Urban Development Areas As A Web-based Service,
ISPRS16(B4: 227-233).
DOI Link 1610

Zou, X.L.[Xiao-Liang], Zhao, G.H.[Gui-Hua], Li, J.[Jonathan], Yang, Y.[Yuanxi], Fang, Y.[Yong],
Object Based Image Analysis Combining High Spatial Resolution Imagery And Laser Point Clouds For Urban Land Cover,
ISPRS16(B3: 733-739).
DOI Link 1610

Peng, F.F.[Fei-Fei], Gong, J.Y.[Jian-Ya], Wang, L.[Le], Wu, H.[Huayi], Yang, J.[Jiansi],
Impact Of Building Heights On 3d Urban Density Estimation From Spaceborne Stereo Imagery,
ISPRS16(B3: 677-683).
DOI Link 1610

Núñez, J.M.[Juan Manuel],
Segmentation of Urban Impervious Surface Using Cellular Neural Networks,
Springer DOI 1511

Volpi, M.[Michele], Ferrari, V.[Vittorio],
Semantic segmentation of urban scenes by learning local class interactions,
Image segmentation BibRef

Kumar, U., Milesi, C., Nemani, R.R., Basu, S.,
Multi-sensor multi-resolution image fusion for improved vegetation and urban area classification,
DOI Link 1508

Kumar, U., Milesi, C., Nemani, R.R., Kumar Raja, S., Ganguly, S., Wang, W.,
Sparse unmixing via variable splitting and augmented Lagrangian for vegetation and urban area classification using Landsat data,
DOI Link 1508

Sakurada, K.[Ken], Okatani, T.[Takayuki], Kitani, K.M.[Kris M.],
Massive City-Scale Surface Condition Analysis Using Ground and Aerial Imagery,
ACCV14(I: 49-64).
Springer DOI 1504

Akin, A., Erdogan, M.A., Berberoglu, S.,
The Spatiotemporal Land use/cover Change of Adana City,
DOI Link 1402

Badawy, H.M., Moussa, A., El-Sheimy, N.,
Automatic Classification of coarse density LiDAR data in urban area,
DOI Link 1411
buildings, vehicles, trees and roads without RGB. BibRef

Kouchi, H.S.[H. Salimi], Sahebi, M.R., Abkar, A.A., Valadan Zoej, M.J.,
Fractional Vegetation Cover Estimation In Urban Environments,
HTML Version. 1311

Tompalski, P., Wezyk, P.,
LIDAR and VHRS Data for Assessing Living Quality in Cities: An Approach Based on 3D Spatial Indices,
DOI Link 1209

Shekhar, S.,
Detecting Slums From Quick Bird Data In Pune Using An Object Oriented Approach,
DOI Link 1209

Bechtel, B., Langkamp, T., Böhner, J., Daneke, C., Ossenbrügge, J., Schempp, S.,
Classification and Modelling of Urban Micro-Climates Using Multisensoral and Multitemporal Remote Sensing Data,
DOI Link 1209

Guan, H., Yu, J., Li, J., Luo, L.,
Random Forests-based Feature Selection For Land-Use Classification Using Lidar Data And Orthoimagery,
DOI Link 1209

Shi, L.,
The Low Backscattering Targets Classification In Urban Areas,
AnnalsPRS(I-7), No. 2012, pp. 171-176.
HTML Version. 1209

Walde, I., Hese, S., Berger, C., Schmullius, C.,
Graph-based Urban Land Use Mapping From High Resolution Satellite Images,
AnnalsPRS(I-4), No. 2012, pp. 119-124.
HTML Version. 1209

Jiang, L., Gu, J., Chen, X., You, Y., Tang, Q.,
A Study Of Urban Intensive Land Evaluating System,
AnnalsPRS(I-4), No. 2012, pp. 19-22.
HTML Version. 1209

Elsharkawy, A., Elhabiby, M., El-sheimy, N.,
New Combined Pixel/object-based Technique For Efficient Urban Classsification Using Worldview-2 Data,
DOI Link 1209

Bekkari, A.[Aissam], Idbraim, S.[Soufiane], Elhassouny, A.[Azeddine], Mammass, D.[Driss], El yassa, M.[Mostafa], Ducrot, D.[Danielle],
SVM and Haralick Features for Classification of High Resolution Satellite Images from Urban Areas,
Springer DOI 1208

Le Bris, A., Robert-Sainte, P.,
Classification of Roof Materials for Rainwater Pollution Modelization,
PDF File. 0906

Hese, S., Voltersen, M., Lindner, M., Berger, C.,
TerraSAR-X and RapidEye data for the parameterisation of relational characteristics of urban ATKIS DLM objects,
PDF File. 1106
digital landscape model. BibRef

Hermosilla, T.[Txomin], Ruiz, L.A.[Luis A.], Recio, J.A., Balsa-Barreiro, J.,
Land-use Mapping of Valencia City Area from Aerial Images and LiDAR Data,
WWW Link. Remote Sensing, Classification, Urban areas 1204

Hermosilla, T.[Txomin], Ruiz, L.A.[Luis A.], Recio, J.A., Cambra López, M.,
Efficiency of Context-Based Attributes for Land Use Classification of Urban Environments,
PDF File. 1106

Kux, H.J.H., Novack, T., Ferreira, R., Oliveira, D.A.,
Urban Land Cover Classification Using Optical VHR Data and the Knowledge-Based System Interimage,
PDF File. 1007

Novack, T., Kux, H.J.H., Feitosa, R.Q., Costa, G.A.,
Per Block Urban Land Use Interpretation Using Optical VHR Data and the Knowledge-Based System Interimage,
PDF File. 1007

Cui, H.S.[Hai-Shan], Qian, H.S.[Huai-Sui], Qian, L.X.[Le-Xiang], Li, Y.[Ying],
Remote Sensing Experts Classification System Applying in the Land Use Classification in Guangzhou City,

Mavrantza, O.D., Argialas, D.P.,
Identification of Urban Features Using Object-Oriented Image Analysis,
PDF File. 0711
See also Object-Oriented Image Analysis for the Identification of Geologic Lineaments. BibRef

Mavrantza, O.D., Charou, E., Stefouli, M.,
Object-oriented image analysis of land cover for multi-temporal monitoring. Case study: Zakynthos Island, Greece,
PDF File. 0607

Yokota, S., Takeuchi, K.,
Study on the relationship between landscape characteristics of fragmented urban green spaces and distribution of urban butterflies - Application of object-based satellite image analysis,
PDF File. 0607

Kux, H., Araújo, E.,
Multi-temporal object-oriented classifications and analysis of Quickbird scenes at a metropolitan area in Brazil (Belo Horizonte, Minas Gerais State),
PDF File. 0607

Kux, H., Pinho, C.,
Object-oriented analysis of high-resolution satellite images for intra-urban land cover classification: case study in São José dos campos, São Paulo State, Brazil,
PDF File. 0607

Pesaresi, M.[Martino],
Textural Classification of Very High-resolution Satellite Imagery: Empirical Estimation of the Relationship Between Window Size and Detection Accuracy in Urban Environment,
IEEE DOI BibRef 9900

Chapter on Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR continues in
Wetlands, Wetland Detection, Analysis .

Last update:Mar 13, 2017 at 16:25:24