Keith Price Bibliography Classification for Urban Area Land Cover, Remote Sensing

22.1.1.4 Classification for Urban Area Land Cover, Remote Sensing

Chapter Contents (Back)
Classification. Remote Sensing. See also General Urban Area Detection.

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
BibRef

Heikkonen, J.[Jukka], Varfis, A.[Aristide], and Kanellopoulos, I.[Ioannis],
A Method for Remote Sensing Based Classification of Urban Areas,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

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
BibRef

Hasse, J.[John], Lathrop, R.G.[Richard G.],
A Housing-Unit-Level Approach to Characterizing Residential Sprawl,
PhEngRS(69), No. 9, September 2003, pp. 1021-1030.
WWW Version. 0309
Spatial measurements of new housing units provide a means for assessing the degree to which new residential development can be characterized as sprawling. BibRef

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 Version. 0509
BibRef

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 Version. 0509
BibRef

Zhang, L.P.[Liang-Pei], Huang, X., Huang, B.[Bo], Li, P.X.[Ping-Xiang],
A Pixel Shape Index Coupled With Spectral Information for Classification of High Spatial Resolution Remotely Sensed Imagery,
GeoRS(44), No. 10, October 2006, pp. 2950-2961.
IEEE DOI Link 0609
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 Version. 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.
IEEE DOI Link 0812
BibRef

Zhao, Y.[Yindi], 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.
IEEE DOI Link 0704
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 Version. 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 Version. 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 Version. 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.
IEEE DOI Link 0810
BibRef

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 Version. 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 Version. 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 Version. 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 Version. 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 Version. 0804
Three machine learning subpixel estimation methods were applied to estimate urban cover and the resulting predictions were compared based on accuracy. BibRef

Nichol, J.[Janet],
An Emissivity Modulation Method for Spatial Enhancement of Thermal Satellite Images in Urban Heat Island Analysis,
PhEngRS(75), No. 5, May 2009, pp. 547-556.
WWW Version. 0904
A methodology using ancillary land-cover information to enhance the spatial resolution of thermal satellite images for detailed studies of land surface temperature, such as in urban heat island analysis. 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 Link
WWW Version. 1109
Image segmentation; Differential morphological profile (DMP); Very high resolution (VHR) images; Mathematical morphology Morphology to get scale. BibRef