- -

Change detection of isolated housing using a new hybrid approach based on object classification with optical and TerraSAR-X data

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Change detection of isolated housing using a new hybrid approach based on object classification with optical and TerraSAR-X data

Show simple item record

Files in this item

dc.contributor.author Vidal Pantaleoni, Ana es_ES
dc.contributor.author Moreno Cambroreno, María del Rocío es_ES
dc.date.accessioned 2015-11-04T11:52:31Z
dc.date.available 2015-11-04T11:52:31Z
dc.date.issued 2011-12
dc.identifier.issn 0143-1161
dc.identifier.uri http://hdl.handle.net/10251/56993
dc.description.abstract Optical and microwave high spatial resolution images are now available for a wide range of applications. In this work, they have been applied for the semi-automatic change detection of isolated housing in agricultural areas. This article presents a new hybrid methodology based on segmentation of high-resolution images and image differencing. This new approach mixes the main techniques used in change detection methods and it also adds a final segmentation process in order to classify the change detection product. First, isolated building classification is carried out using only optical data. Then, synthetic aperture radar (SAR) information is added to the classification process, obtaining excellent results with lower complexity cost. Since the first classification step is improved, the total change detection scheme is also enhanced when the radar data are used for classification. Finally, a comparison between the different methods is presented and some conclusions are extracted from the study. © 2011 Taylor & Francis. es_ES
dc.language Inglés es_ES
dc.publisher Taylor & Francis (Routledge): STM, Behavioural Science and Public Health Titles es_ES
dc.relation.ispartof International Journal of Remote Sensing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Agricultural areas es_ES
dc.subject Change detection es_ES
dc.subject Classification process es_ES
dc.subject High resolution image es_ES
dc.subject High spatial resolution images es_ES
dc.subject Hybrid approach es_ES
dc.subject Hybrid methodologies es_ES
dc.subject Image differencing es_ES
dc.subject Isolated buildings es_ES
dc.subject Lower complexity es_ES
dc.subject Object classification es_ES
dc.subject Optical data es_ES
dc.subject Radar data es_ES
dc.subject Segmentation process es_ES
dc.subject Semi-automatics es_ES
dc.subject Techniques used es_ES
dc.subject TerraSAR-X es_ES
dc.subject Housing es_ES
dc.subject Image segmentation es_ES
dc.subject Signal detection es_ES
dc.subject Synthetic aperture radar es_ES
dc.subject Agricultural land es_ES
dc.subject Building es_ES
dc.subject Complexity es_ES
dc.subject Detection method es_ES
dc.subject Housing market es_ES
dc.subject Microwave imagery es_ES
dc.subject Satellite imagery es_ES
dc.subject Spatial resolution es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Change detection of isolated housing using a new hybrid approach based on object classification with optical and TerraSAR-X data es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1080/01431161.2011.571297
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Vidal Pantaleoni, A.; Moreno Cambroreno, MDR. (2011). Change detection of isolated housing using a new hybrid approach based on object classification with optical and TerraSAR-X data. International Journal of Remote Sensing. 32(24):9621-9635. doi:10.1080/01431161.2011.571297 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1080/01431161.2011.571297 es_ES
dc.description.upvformatpinicio 9621 es_ES
dc.description.upvformatpfin 9635 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 32 es_ES
dc.description.issue 24 es_ES
dc.relation.senia 221979 es_ES
dc.identifier.eissn 1366-5901
dc.description.references BLAES, X., VANHALLE, L., & DEFOURNY, P. (2005). Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment, 96(3-4), 352-365. doi:10.1016/j.rse.2005.03.010 es_ES
dc.description.references Chen, Y., Shi, P., Fung, T., Wang, J., & Li, X. (2007). Object‐oriented classification for urban land cover mapping with ASTER imagery. International Journal of Remote Sensing, 28(20), 4645-4651. doi:10.1080/01431160500444731 es_ES
dc.description.references Dalla Mura, M., Benediktsson, J. A., Bovolo, F., & Bruzzone, L. (2008). An Unsupervised Technique Based on Morphological Filters for Change Detection in Very High Resolution Images. IEEE Geoscience and Remote Sensing Letters, 5(3), 433-437. doi:10.1109/lgrs.2008.917726 es_ES
dc.description.references Dell’Acqua, F., & Gamba, P. (2006). Discriminating urban environments using multiscale texture and multiple SAR images. International Journal of Remote Sensing, 27(18), 3797-3812. doi:10.1080/01431160600557572 es_ES
dc.description.references Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610-621. doi:10.1109/tsmc.1973.4309314 es_ES
dc.description.references Im, J., Jensen, J. R., & Tullis, J. A. (2008). Object‐based change detection using correlation image analysis and image segmentation. International Journal of Remote Sensing, 29(2), 399-423. doi:10.1080/01431160601075582 es_ES
dc.description.references Lhomme, S., He, D., Weber, C., & Morin, D. (2009). A new approach to building identification from very‐high‐spatial‐resolution images. International Journal of Remote Sensing, 30(5), 1341-1354. doi:10.1080/01431160802509017 es_ES
dc.description.references LOBO, A., CHIC, O., & CASTERAD, A. (1996). Classification of Mediterranean crops with multisensor data: per-pixel versus per-object statistics and image segmentation. International Journal of Remote Sensing, 17(12), 2385-2400. doi:10.1080/01431169608948779 es_ES
dc.description.references Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365-2401. doi:10.1080/0143116031000139863 es_ES
dc.description.references Shimabukuro, Y. E., Almeida‐Filho, R., Kuplich, T. M., & de Freitas, R. M. (2007). Quantifying optical and SAR image relationships for tropical landscape features in the Amazônia. International Journal of Remote Sensing, 28(17), 3831-3840. doi:10.1080/01431160701236829 es_ES
dc.description.references Stramondo, S., Bignami, C., Chini, M., Pierdicca, N., & Tertulliani, A. (2006). Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies. International Journal of Remote Sensing, 27(20), 4433-4447. doi:10.1080/01431160600675895 es_ES
dc.description.references Yuan, D., & Elvidge, C. D. (1996). Comparison of relative radiometric normalization techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 51(3), 117-126. doi:10.1016/0924-2716(96)00018-4 es_ES


This item appears in the following Collection(s)

Show simple item record