Tytuł artykułu

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Tytuł czasopisma

Geodesy and Cartography

Rocznik

2016

Wolumin

vol. 65

Numer

No 2

Autorzy

Drzewiecki, Wojciech

Słowa kluczowe

machine learning ; model ensembles ; sub-pixel classification ; impervious areas ; Landsat

Wydział PAN

Nauki Techniczne

Zakres

193-218

Wydawca

Polska Akademia Nauk/ Komitet Geodezji Polskiej Akademii Nauk; Polish Academy of Sciences / Commitee on Geodesy Polish Academy of Sciences

Data

2016

Typ

Artykuły / Articles

Identyfikator

DOI: 10.1515/geocart-2016-0016 ; ISSN 2080-6736

Źródło

Geodesy and Cartography; 2016; vol. 65; No 2; 193-218

Referencje

Güneralp (2014), Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling of and, International Journal Applied Earth Observation Geoinformation, 33, 119, doi.org/10.1016/j.jag.2014.05.004 ; Caldwell (2012), Impacts of impervious cover , water withdrawals , and climate change on river flows in the conterminous US Hydrol, Earth Syst, 16, 2839, doi.org/10.5194/hess-16-2839-2012 ; Friedman (2002), Stochastic Gradient Boosting and Data Analysis, Computational Statistics, 38, 367. ; Qi (2007), Research on SVM ensemble and its application to remote sensing classification In : Proceedings of the on Intelligent Systems and Knowledge Engineering, International Conference ISKE, doi.org/10.2991/iske.2007.102 ; Lu (2014), Methods to extract impervious surface areas from satellite images of Digital Earth, International Journal, 7, 93, doi.org/10.1080/17538947.2013.866173 ; Boser (1992), Training Algorithm for Optimal Margin Classifiers In : Proceedings of the Fifth Annual Workshop on Computational Learning, Theory, 144. ; Kuhn (2008), Building Predictive Models in R Using the caret Package, Journal of Statistical Software, 28, 1, doi.org/10.18637/jss.v028.i05 ; Shahtahmassebi (2016), Remote sensing of impervious surface growth : A framework for quantifying urban expansion and re - densification mechanisms of and, International Journal Applied Earth Observation Geoinformation, 46, 94, doi.org/10.1016/j.jag.2015.11.007 ; Liu (2005), Comparison of non - linear mixture models : sub - pixel classification of, Remote Sensing Environment, 94, 145, doi.org/10.1016/j.rse.2004.09.004 ; Galeana (2016), Remote Sensing - Based Biomass Estimation In Marghany ed ) Environmental Applications of Remote Sensing online, InTech, doi.org/10.5772/61813.Availableat:http://www.intechopen.com/books/environmental-applications-of-remote-sensing/remote-sensing-based-biomass-estimation ; García (2005), Cooperative coevolution of artificial neural network ensembles for pattern classification, IEEE Trans, 9, 271. ; Weng (2012), Remote sensing of impervious surface in the urban areas : Requirements , methods and trends of, Remote Sensing Environment, 117, 34, doi.org/10.1016/j.rse.2011.02.030 ; Rooney (2004), Dynamic integration of regression models In : Proceedings of the International Workshop on Multiple Classifier Systems in vol Springer pp, Lecture Notes Computer Science, 164, doi.org/10.1007/978-3-540-25966-4_16 ; Gómez (2013), and Camps - Advances in synergy of AATSR - MERIS sensors for cloud detection In Sensing Symposium Jul pp, IEEE Int, 4391. ; Coelho (2006), and Von Zuben The influence of the pool of candidates on the performance of selection and combination techniques in ensembles In : Proceedings of the International Joint Conference on Neural, Networks, 10588. ; Bernat (2014), Two - stage subpixel impervious surface coverage estimation : comparing classification and regression trees and artificial neural networks In : Proc SPIE Vol Image and Signal Processing for, Remote Sensing, 9244. ; Homer (2007), Completion of the National Land Cover Database for the Conterminous United States and, Photogrammetric Engineering Remote Sensing, 73, 337. ; Wichard (2003), Building ensembles with heterogeneous models In : Course of the International School on Neural Available online at : http : / / www wichard de / publications / salerno lncs pdf, Nets, 22, 2003. ; Ridd (1995), Exploring a V vegetation - impervious surface - soil ) model for urban ecosystem analysis through remote sensing : Comparative anatomy for cities, Remote Sens, 16, 2165, doi.org/10.1080/01431169508954549 ; Lu (2014), Current situation and needs of change detection techiques of Image and Data Fusion, International journal, 5, 1, doi.org/10.1080/19479832.2013.868372 ; Xu (2006), Modification of normalized difference water index ( NDWI ) to enhance open water features in remotely sensed imagery of, International Journal Remote Sensing, 27, 3025, doi.org/10.1080/01431160600589179 ; Deng (2013), The use of single - date MODIS imagery for estimating large - scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques of and, ISPRS Journal Photogrammetry Remote Sensing, 86, 100, doi.org/10.1016/j.isprsjprs.2013.09.010 ; Walton (2008), Subpixel urban land cover estimation : comparing Cubist , Random Forest and Support Vector Regression and, Photogrammetric Engineering Remote Sensing, 75, 1213, doi.org/10.14358/PERS.74.10.1213 ; Tewkesbury (2015), A critical synthesis of remotely sensed optical image change detection techniques of, Remote Sensing Environment, 160, doi.org/10.1016/j.rse.2015.01.006 ; Smola (2004), A tutorial on support vector regression and, Statistics Computing, 14, 199, doi.org/10.1023/B:STCO.0000035301.49549.88 ; Mountrakis (2009), Developing collaborative classifiers using an expert - based model and, Photogrammetric Engineering Remote Sensing, 75, 831, doi.org/10.14358/PERS.75.7.831 ; Chormański (2008), de Improving distributed runoff prediction in urbanized catchments with remote sensing based estimates of impervious surface cover, Sensors, 8, 910, doi.org/10.3390/s8020910 ; Breiman (2001), Random Forests, Machine Learning, 45, 5, doi.org/10.1023/A:1010933404324 ; Shao (2011), Sub - pixel mapping of tree canopy , impervious surfaces , and cropland in the Laurentian Great Lakes Basin using MODIS time - series data of Selected Topics in Applied and, IEEE Journal Earth Observation Remote Sensing, 4, 336, doi.org/10.1109/JSTARS.2010.2062173 ; Deng (2012), BCI : A biophysical composition index for remote sensing of urban environments of, Remote Sensing Environment, 127, 247, doi.org/10.1016/j.rse.2012.09.009 ; Esch (2008), Wehrmann Model - based estimation of impervious surface by application of support vector machines The International Archives of the Photogrammetry , Remote Sensing and Spatial Information XXXVII Part, Sciences, 8, 41. ; Xi (2011), Estimation of impervious surface based on integrated analysis of classification and regression by using SVM In : Geoscience and Remote Sensing Symposium pp, IEEE International, 2809. ; Engler (2013), Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution and, Forest Ecology Management, 310, doi.org/10.1016/j.foreco.2013.07.059 ; Arnold (1996), Impervious surface coverage : the emergence of a key environmental indicator the, Journal of American Planning Association, 62, 243, doi.org/10.1080/01944369608975688 ; Dunn (1961), Multiple comparisons among means the, Journal of American Statistical Association, 56, 293, doi.org/10.1080/01621459.1961.10482090 ; Heremans (2015), Machine learning methods for sub - pixel land - cover classification in the spatially heterogeneous region of Flanders a multi - criteria comparison of, International Journal Remote Sensing, 36, 2934, doi.org/10.1080/01431161.2015.1054047 ; Mohapatra (2010), High resolution impervious surface estimation : An integration of Ikonos and Landsat - ETM imagery and, Photogrammetric Engineering Remote Sensing, 76, 1329, doi.org/10.14358/PERS.76.12.1329 ; Tokarczyk (2015), High - quality observation of surface imperviousness for urban runoff modelling using UAV imagery Hydrol, Earth Syst, 19, 4215, doi.org/10.5194/hess-19-4215-2015 ; Hussain (2013), Change detection from remotely sensed images : From pixel - based to object - based approaches of and, ISPRS Journal Photogrammetry Remote Sensing, 80, 91, doi.org/10.1016/j.isprsjprs.2013.03.006 ; Dams (2013), Mapping impervious surface change from remote sensing for hydrological modeling, Journal of Hydrology, 485, doi.org/10.1016/j.jhydrol.2012.09.045 ; Friedman (1991), Multivariate Adaptive Regression Splines The of, Annals Statistics, 19, 1, doi.org/10.1214/aos/1176347963 ; Yang (2003), Urban land - cover change detection through sub - pixel imperviousness mapping using remotely sensed data and, Photogrammetric Engineering Remote Sensing, 69, 1003, doi.org/10.14358/PERS.69.9.1003 ; Zhang (2010), Multi - source remote sensing data fusion : status and trends of Image and Data Fusion, International Journal, 1, 1. ; Partalas (2008), Greeedy regression ensemble selection : Theory and application to water quality prediction, Information Sciences, 178. ; Li (2011), Random KNN feature selection a fast and stable alternative to Random Forests, BMC Bioinformatics, 12, 450, doi.org/10.1186/1471-2105-12-450 ; Hothorn (2005), The design and analysis of benchmark experiments and, Journal of Computational Graphical Statistics, 14, 675, doi.org/10.1198/106186005X59630

O czasopiśmie

The Advances in Geodesy and Geoinformation (formerly “Geodesy and Cartography”) is an open access international journal (semiannual) concerned with the study of scientific problems in the field of geodesy, geoinformation and their related interdisciplinary sciences. The journal has a rigorous peer–review process to ensure the best research publications. It is publishing peer–reviewed original articles on theoretical or modelling studies, and on results of experiments associated with geodesy and geodynamics, geoinformation, cartography and GIS, cadastre and land management, photogrammetry, remote sensing and related disciplines. Besides original research articles, the Advances in Geodesy and Geoinformation also accepts review articles on topical subjects, short notes/letters and communication of a great importance to the readers, and special issues arising from the national/international conferences as well as collection of articles that concentrates on a hot topical research area that falls within the scope of the journal.

Content of Advances in Geodesy and Geoinformation is archived with a long-term preservation service by the National Library of Poland.