403
Views
12
CrossRef citations to date
0
Altmetric
Original Articles

Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method

, ORCID Icon & ORCID Icon
Pages 1067-1087 | Received 06 Jul 2018, Accepted 19 Dec 2018, Published online: 18 Mar 2019

References

  • Aksoy S, Haralick RM. 2001. Feature normalization and likelihood-based similarity measures for image retrieval. Pattern Recognit Lett. 22(5):563–582.
  • Amini J, Saradjian MR, Blais JAR, Lucas C, Azizi A. 2002. Automatic road-side extraction from large scale imagemaps. Int J Appl Earth Obs Geoinf. 4(2):95–107.
  • Angiuli E, Trianni G. 2013. Urban mapping in Landsat images based on normalized difference spectral vector. IEEE Geosci Remote Sens Lett. 11(3):661–665.
  • Bhatti SS, Tripathi NK. 2014. Built-up area extraction using landsat 8 OLI imagery. GIScience Remote Sens. 51(4):445–467.
  • Cao X, Chen J, Imura H, Higashi O. 2009. A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data. Remote Sens Environ. 113(10):2205–2209.
  • Clausi DA. 2002. An analysis of co-occurrence texture statistics as a function of grey level quantization. Can J Remote Sens. 28(1):45–62.
  • Congalton RG. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ. 37(1):35–46.
  • Fan F, Fan W, Weng Q. 2015. Improving urban impervious surface mapping by linear spectral mixture analysis and using spectral indices. Can J Remote Sens. 41(6):577–586.
  • Foody GM, Arora MK. 1997. An evaluation of some factors affecting the accuracy of classification by an artificial neural network. Int J Remote Sens. 18(4):799–810.
  • Fukunaga K. 1990. Introduction to statistical pattern recognition. San Diego, CA: Academic Press Professional, Inc.
  • Gong P, Marceau DJ, Howarth PJ. 1992. A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data. Remote Sens Environ. 40(2):137–151.
  • Gopal S, Woodcock C. 1996. Remote sensing of forest change using artificial neural networks. IEEE Trans Geosci Remote Sens. 34(2):398–404.
  • Graf ABA, Smola AJ, Borer S. 2003. Classification in a normalized feature space using support vector machines. IEEE Trans Neural Netw. 14(3):597–605.
  • Guyon I, Aliferis C, André E. 2007. Computational methods of feature selection. In: Motoda H, Liu H, editors. Data mining and knowledge discovery series. Chapman and Hall/CRC.
  • Hall-Beyer M. 2017. Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales. Int J Remote Sens. 38(5):1312–1338.
  • Haralick RM, Shanmugam K, Dinstein I. 1973. Textural features for image classification. IEEE Trans Syst Man Cybern. 3(6):610–621.
  • Huang C, Davis LS, Townshend JRG. 2002. An assessment of support vector machines for land cover classification. Int J Remote Sens. 23(4):725–749.
  • Huang X, Zhang L, Li P. 2007. Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery. IEEE Geosci Remote Sens Lett. 4(2):260–264.
  • Hughes GF. 1968. On the mean accuracy of statistical pattern recognizers. IEEE Trans Inform Theory. 14(1):55–63.
  • Ilsever M, Unsalan C. 2013. Locating the urban area in satellite images to detect changes in them. Proceedings of the 6th International Conference on Recent Advances in Space Technologies (RAST); June 12–14; Istanbul, Turkey: IEEE; p. 109–114.
  • Jimenez-Rodriguez LO, Arzuaga-Cruz E, Velez-Reyes M. 2007. Unsupervised linear feature-extraction methods and their effects in the classification of high-dimensional data. IEEE Trans Geosci Remote Sensing. 45(2):469–483.
  • Kuffer M, Pfeffer K, Sliuzas R, Baud I. 2016. Extraction of slum areas from vhr imagery using GLCM variance. IEEE J Sel Top Appl Earth Obs Remote Sens. 9(5):1830–1840.
  • Kwak N, Choi CH. 2002. Input feature selection for classification problems. IEEE Trans Neural Netw. 13(1):143–159.
  • Li E, Du P, Samat A, Xia J, Che M. 2015. An automatic approach for urban land-cover classification from Landsat-8 OLI data. Int J Remote Sens. 36(24):5983–6007.
  • Lu D, Tian H, Zhou G, Ge H. 2008. Regional mapping of human settlements in southeastern China with multisensor remotely sensed data. Remote Sens Environ. 112(9):3668–3679.
  • Lu D, Weng Q. 2006. Use of impervious surface in urban land-use classification. Remote Sens Environ. 102(1–2):146–160.
  • Melgani F, Bruzzone L. 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens. 42(8):1778–1790.
  • Miller RB, Small C. 2003. Cities from space: Potential applications of remote sensing in urban environmental research and policy. Environ Sci Policy. 6(2):129–137.
  • Molina DE, Gleich D, Datcu M. 2010. gibbs random field models for model-based despeckling of SAR images. IEEE Geosci Remote Sens Lett. 7(1):73–77.
  • Patel N, Mukherjee R. 2015. Extraction of impervious features from spectral indices using artificial neural network. Arab J Geosci. 8(6):3729–3741.
  • 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 Obs Remote Sens. 4(4):923–934.
  • Pohjalainen J, Räsänen O, Kadioglu S. 2015. Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput Speech Lang. 29(1):145–171.
  • Pudil P, Novovičová J, Kittler J. 1994. Floating search methods in feature selection. Pattern Recognit Lett. 15(11):1119–1125.
  • Schneider A, Friedl MA, Potere D. 2009. A new map of global urban extent from MODIS satellite data. Environ Res Lett. 4(4):044003.
  • Shahshahani BM, Landgrebe DA. 1993. Use of unlabeled samples for mitigating the hughes phenomenon. Proceeding of IGARSS’93- IEEE International Geoscience and Remote Sensing Symposium; Aug 18–21; Tokyo, Japan: IEEE; p. 1535–1537.
  • Shi K, Huang C, Yu B, Yin B, Huang Y, Wu J. 2014. Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas. Remote Sens Lett. 5(4):358–366.
  • Sirmacek B, Unsalan C. 2009. Urban-area and building detection using SIFT keypoints and graph theory. IEEE Trans Geosci Remote Sens. 47(4):1156–1167.
  • Tong X, Zhang X, Liu M. 2010. Detection of urban sprawl using a genetic algorithm-evolved artificial neural network classification in remote sensing: a case study in Jiading and Putuo districts of Shanghai, China. Int J Remote Sens. 31(6):1485–1504.
  • Vapnik V. 2013. The nature of statistical learning theory. New York, NY: Springer-Verlag.
  • Waske B, van der Linden S, Benediktsson JA, Rabe A, Hostert P. 2010. Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Trans Geosci Remote Sens. 48(7):2880–2889.
  • Whitney AW. 1971. A direct method of nonparametric measurement selection. IEEE Trans Comput. 20(9):1100–1103.
  • Zha Y, Gao J, Ni S. 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens. 24(3):583–594.
  • Zhang H, Lin H, Li Y. 2015. Impacts of feature normalization on optical and SAR data fusion for land use/land cover classification. IEEE Geosci Remote Sens Lett. 12(5):1061–1065.
  • Zhang J, Li P, Wang J. 2014. Urban built-up area extraction from Landsat TM/ETM + Images Using Spectral Information and Multivariate Texture. Remote Sens. 6(8):7339–7359.
  • Zhang Y, Guindon B. 2012. Multispectral analysis for manmade surface extraction from RapidEye and SPOT5. Can J Remote Sens. 38(2):180–196.
  • Zhao Y, Zhang L, Li P, Huang B. 2007. Classification of high spatial resolution imagery using improved Gaussian Markov Random-field-based texture features. IEEE Trans Geosci Remote Sens. 45(5):1458–1468.
  • Zhong P, Wang R. 2007. A multiple conditional random fields ensemble model for urban area detection in remote sensing optical images. IEEE Trans Geosci Remote Sens. 45(12):3978–3988.
  • Zhou W. 2013. An Object-Based approach for urban land cover classification: Integrating LiDAR Height and Intensity Data. IEEE Geosci Remote Sens Lett. 10(4):928–931.
  • Zhou Y, Yang G, Wang S, Wang L, Wang F, Liu X. 2014. A new index for mapping built-up and bare land areas from Landsat-8 OLI data. Remote Sens Lett. 5(10):862–871.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.