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Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 46, 2020 - Issue 6
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Articles

Object-Based Wetland Classification Using Multi-Feature Combination of Ultra-High Spatial Resolution Multispectral Images

Classification orientée-objet des milieux humides à l’aide d’une combinaison multi-paramètres d’images multispectrales à résolution spatiale ultra-haute

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Pages 784-802 | Received 25 Sep 2020, Accepted 03 Jan 2021, Published online: 19 Jan 2021

References

  • Alvarez-Vanhard, E., Houet, T., Mony, C., Lecoq, L., and Corpetti, T. 2020. “Can UAVs fill the gap between in situ surveys and satellites for habitat mapping.” Remote Sensing of Environment, Vol. 243: pp. 111780. doi:10.1016/j.rse.2020.111780.
  • Anderson, K., and Gaston, K.J. 2013. “Lightweight unmanned aerial vehicles will revolutionize spatial ecology.” Frontiers in Ecology and the Environment, Vol. 11(No. 3): pp. 138–146. doi:10.1890/120150.
  • Alvarez-Cobelas, M., Sánchez-Carrillo, S., Cirujano, S., and Angeler, D.G. 2007. “Long-term changes in spatial patterns of emergent vegetation in a Mediterranean floodplain: Natural versus anthropogenic constraints.” Plant Ecology, Vol. 194(No. 2): pp. 257–271. doi:10.1007/s11258-007-9289-6.
  • Abeysinghe, T., Simic Milas, A., Arend, K., Hohman, B., Reil, P., Gregory, A., and Vázquez-Ortega, A. 2019. “Mapping invasive phragmites australis in the old woman creek estuary using UAV remote sensing and machine learning classifiers.” Remote Sensing, Vol. 11(No. 11): pp. 1380. doi:10.3390/rs11111380.
  • Brook, A., De Micco, V., Battipaglia, G., Erbaggio, A., Ludeno, G., Catapano, I., and Bonfante, A. 2020. “A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard.” Remote Sensing of Environment, Vol. 240: pp. 111679. doi:10.1016/j.rse.2020.111679.
  • Belgiu, M., and Drăguţ, L. 2016. “Random forest in remote sensing: A review of applications and future directions.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 114: pp. 24–31. doi:10.1016/j.isprsjprs.2016.01.011.
  • Blaschke, T. 2010. “Object based image analysis for remote sensing.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65(No. 1): pp. 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
  • Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M.L., and Bareth, G. 2015. “Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley.” International Journal of Applied Earth Observation and Geoinformation, Vol. 39: pp. 79–87. doi:10.1016/j.jag.2015.02.012.
  • Belgiu, M., and Drăguţ, L. 2014. “Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 96: pp. 67–75. doi:10.1016/j.isprsjprs.2014.07.002.
  • Breiman, L. 1996. “Bagging predictors.” Machine Learning, Vol. 24(No. 2): pp. 123–140. doi:10.1007/BF00058655.
  • Breiman, L. 2001. “Random forests.” Machine Learning, Vol. 45(No. 1): pp. 5–32. doi:10.1023/A:1010933404324.
  • Chen, Y., He, X., Xu, J., Zhang, R., and Lu, Y. 2020. “Scattering feature set optimization and polarimetric SAR classification using object-oriented RF-SFS algorithm in coastal wetlands.” Remote Sensing, Vol. 12(No. 3): pp. 407. doi:10.3390/rs12030407.
  • Cao, J., Leng, W., Liu, K., Liu, L., He, Z., and Zhu, Y. 2018. “Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models.” Remote Sensing, Vol. 10(No. 2): pp. 89. doi:10.3390/rs10010089.
  • Clark, M.L., Aide, T.M., Grau, H.R., and Riner, G. 2010. “A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America.” Remote Sensing of Environment, Vol. 114(No. 11): pp. 2816–2832. doi:10.1016/j.rse.2010.07.001.
  • Canisius, F., Turral, H., and Mbilinyi, B.P. 2011. “Analysis of seasonal land use in Usangu wetlands, Tanzania: An object-oriented technique for multi-temporal analysis with high-resolution data.” International Journal of Remote Sensing, Vol. 32(No. 7): pp. 1885–1900. doi:10.1080/01431161003639645.
  • de Almeida Furtado, L.F., Silva, T.S.F., and de Moraes Novo, E.M.L. 2016. “Dual-season and full-polarimetric C band SAR assessment for vegetation mapping in the Amazon várzea wetlands.” Remote Sensing of Environment, Vol. 174: pp. 212–222. doi:10.1016/j.rse.2015.12.013.
  • Davidson, N.C. 2014. “How much wetland has the world lost? Long-term and recent trends in global wetland area.” Marine and Freshwater Research, Vol. 65(No. 10): pp. 934–941. doi:10.107/MF14173.
  • Duro, D.C., Franklin, S.E., and Dubé, M.G. 2012. “A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery.” Remote Sensing of Environment, Vol. 118: pp. 259–272. doi:10.1016/j.rse.2011.11.020.
  • Dronova, I. 2015. “Object-based image analysis in wetland research: A review.” Remote Sensing, Vol. 7(No. 5): pp. 6380–6413. doi:10.3390/rs70506380.
  • Drăguţ, L., Csillik, O., Eisank, C., and Tiede, D. 2014. “Automated parameterisation for multi-scale image segmentation on multiple layers.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 88(No. 100):pp. 119–127. doi:10.1016/j.isprsjprs.2013.11.018.
  • Drăguţ, L., Tiede, D., and Levick, S.R. 2010. “ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data.” International Journal of Geographical Information Science, Vol. 24(No. 6): pp. 859–871. doi:10.1080/13658810903174803.
  • Fu, B., Wang, Y., Campbell, A., Li, Y., Zhang, B., Yin, S., Xing, Z., and Jin, X. 2017. “Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data.” Ecological Indicators, Vol. 73: pp. 105–117. doi:10.1016/j.ecolind.2016.09.029.
  • Guo, Z., Shao, X., Xu, Y., Miyazaki, H., Ohira, W., and Shibasaki, R. 2016. “Identification of village building via Google Earth images and supervised machine learning methods.” Remote Sensing, Vol. 8(No. 4): pp. 271. doi:10.3390/rs8040271.
  • Genuer, R., Poggi, J.M., and Tuleau-Malot, C. 2010. “Variable selection using random forests.” Pattern Recognition Letters, Vol. 31(No. 14): pp. 2225–2236. doi:10.1016/j.patrec.2010.03.014.
  • Ghosh, A., and Joshi, P.K. 2014. “A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery.” International Journal of Applied Earth Observation and Geoinformation, Vol. 26: pp. 298–311. doi:10.1016/j.jag.2013.08.011.
  • Huang, M., Chen, N., Du, W., Chen, Z., and Gong, J. 2018. “DMBLC: An indirect urban impervious surface area extraction approach by detecting and masking background land cover on Google Earth Image.” Remote Sensing, Vol. 10(No. 5): pp. 766. doi:10.3390/rs10050766.
  • Hu, Q., Wu, W., Xia, T., Yu, Q., Yang, P., Li, Z., and Song, Q. 2013. “Exploring the use of Google Earth imagery and object-based methods in land use/cover mapping.” Remote Sensing, Vol. 5(No. 11): pp. 6026–6042. doi:10.3390/rs5116026.
  • Hossain, M.D., and Chen, D. 2019. “Segmentation for object-based image analysis (OBIA): A review of algorithms and challenges from remote sensing perspective.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 150: pp. 115–134. doi:10.1016/j.isprsjprs.2019.02.009.
  • Ji, W., Xu, X., and Murambadoro, D. 2015. “Understanding urban wetland dynamics: Cross-scale detection and analysis of remote sensing.” International Journal of Remote Sensing, Vol. 36(No. 7): pp. 1763–1788. doi:10.1080/01431161.2015.1024895.
  • Jenerowicz, A., and Woroszkiewicz, M. 2016. “The pan-sharpening of satellite and UAV imagery for agricultural applications.” In SPIE Remote Sensing; International Society for Optics and Photonics: Bellingham, WA, USA, October 9998, p. 99981S. doi:10.1117/12.2241645.
  • Kaimaris, D., Georgoula, O., Patias, P., and Stylianidis, E. 2011. “Comparative analysis on the archaeological content of imagery from Google Earth.” Journal of Cultural Heritage, Vol. 12(No. 3): pp. 263–269. doi:10.1016/j.culher.2010.12.007.
  • Lawrence, R.L., Wood, S.D., and Sheley, R.L. 2006. “Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest).” Remote Sensing of Environment, Vol. 100(No. 3): pp. 356–362. doi:10.1016/j.rse.2005.10.014.
  • Lv, J., Jiang, W., Wang, W., Wu, Z., Liu, Y., Wang, X., and Li, Z. 2019. “Wetland loss identification and evaluation based on landscape and remote sensing indices in Xiong’an new area.” Remote Sensing, Vol. 11(No. 23): pp. 2834. doi:10.3390/rs11232834.
  • Lou, P., Fu, B., He, H., Li, Y., Tang, T., Lin, X., Fan, D., and Gao, E. 2020. “An optimized object-based random forest algorithm for marsh vegetation mapping using high-spatial-resolution GF-1 and ZY-3 data.” Remote Sensing, Vol. 12(No. 8): pp. 1270. doi:10.3390/rs12081270.
  • Li, D., and Li, M. 2014. “Research advance and application prospect of unmanned aerial vehicle remote sensing system.” Geomatics and Information Science of Wuhan University, Vol. 39(No. 5): pp. 505–513. doi:10.13203/j.whugis20140045.
  • Lu, D., Li, G., Moran, E., Dutra, L., and Batistella, M. 2014. “The roles of textural images in improving land-cover classification in the Brazilian Amazon.” International Journal of Remote Sensing, Vol. 35(No. 24): pp. 8188–8207. doi:10.1080/01431161.2014.980920.
  • Mahdavi, S., Salehi, B., Amani, M., Granger, J.E., Brisco, B., Huang, W., and Hanson, A. 2017. “Object-based classification of wetlands in Newfoundland and Labrador using multi-temporal PolSAR data.” Canadian Journal of Remote Sensing, Vol. 43(No. 5): pp. 432–450. doi:10.1080/07038992.2017.1342206.
  • Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Brisco, B., Homayouni, S., Gill, E., DeLancey, E.R., and Bourgeau-Chavez, L. 2020. “Big data for a big country: The first generation of canadian wetland inventory map at a spatial resolution of 10-m using Sentinel-1 and Sentinel-2 data on the Google Earth engine cloud computing platform.” Canadian Journal of Remote Sensing, Vol. 46(No. 1): pp. 15–33. doi:10.1080/07038992.2019.1711366.
  • Mahdianpari, M., Salehi, B., Mohammadimanesh, F., and Motagh, M. 2017. “Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 130: pp. 13–31. doi:10.1016/j.isprsjprs.2017.05.010.
  • Mering, C., Baro, J., and Upegui, E. 2010. “Retrieving urban areas on Google Earth images: Application to towns of West Africa.” International Journal of Remote Sensing, Vol. 31(No. 22): pp. 5867–5877. doi:10.1080/01431161.2010.512311.
  • Ma, L., Li, M., Ma, X., Cheng, L., Du, P., and Liu, Y. 2017. “A review of supervised object-based land-cover image classification.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 130: pp. 277–293. doi:10.1016/j.isprsjprs.2017.06.001.
  • Mingwu, Z., Haijiang, J., Desuo, C., and Chunbo, J. 2010. “The comparative study on the ecological sensitivity analysis in Huixian karst wetland.” Procedia Environmental Sciences, Vol. 2: pp. 386–398. doi:10.1016/j.proenv.2010.10.043.
  • Mhangara, P., Mapurisa, W., and Mudau, N. 2020. “Comparison of image fusion techniques using Satellite Pour l’Observation de la Terre (SPOT) 6 satellite imagery.” Applied Sciences, Vol. 10(No. 5): pp. 1881. doi:10.3390/app10051881.
  • Moffett, K.B., and Gorelick, S.M. 2013. “Distinguishing wetland vegetation and channel features with object-based image segmentation.” International Journal of Remote Sensing, Vol. 34(No. 4): pp. 1332–1354. doi:10.1080/01431161.2012.718463.
  • Pulighe, G., Baiocchi, V., and Lupia, F. 2016. “Horizontal accuracy assessment of very high resolution Google Earth images in the city of Rome, Italy.” International Journal of Digital Earth, Vol. 9(No. 4): pp. 342–362. doi:10.1080/17538947.2015.1031716.
  • Powers, R.P., Hay, G.J., and Chen, G. 2012. “How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains.” Remote Sensing of Environment, Vol. 117: pp. 135–145. doi:10.1016/j.rse.2011.07.009.
  • Pande-Chhetri, R., Abd-Elrahman, A., Liu, T., Morton, J., and Wilhelm, V.L. 2017. “Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery.” European Journal of Remote Sensing, Vol. 50(No. 1): pp. 564–576. doi:10.1080/22797254.2017.1373602.
  • Rampi, L.P., Knight, J.F., and Pelletier, K.C. 2014. “Wetland mapping in the Upper Midwest United States.” Photogrammetric Engineering & Remote Sensing, Vol. 80(No. 5): pp. 439–448. doi:10.14358/PERS.80.5.439.
  • Szantoi, Z., Escobedo, F.J., Abd-Elrahman, A., Pearlstine, L., Dewitt, B., and Smith, S. 2015. “Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.” Environmental Monitoring and Assessment, Vol. 187(No. 5): pp. 262. doi:10.1007/s10661-015-4426-5.
  • Tao, Z., Jun, L., Keming, Y., Wenshan, L., and Yuyu, Z. 2015. “Fusion Algorithm for hyperspectral remote sensing image combined with harmonic analysis and Gram-Schmidt transform.” Acta Geodaetica et Cartographica Sinica, Vol. 44(No. 9): pp. 1042. doi:10.11947/j.AGCS.2015.20140637.
  • Thonfeld, F., Steinbach, S., Muro, J., and Kirimi, F. 2020. “Long-term land use/land cover change assessment of the Kilombero catchment in Tanzania using random forest classification and robust change vector analysis.” Remote Sensing, Vol. 12(No. 7): pp. 1057. doi:10.3390/rs12071057.
  • Tian, Y., Jia, M., Wang, Z., Mao, D., Du, B., and Wang, C. 2020. “Monitoring invasion process of Spartina alterniflora by seasonal Sentinel-2 imagery and an object-based random forest classification.” Remote Sensing, Vol. 12(No. 9): pp. 1383. doi:10.3390/rs12091383.
  • Whyte, A., Ferentinos, K.P., and Petropoulos, G.P. 2018. “A new synergistic approach for monitoring wetlands using Sentinels-1 and 2 data with object-based machine learning algorithms.” Environmental Modelling & Software, Vol. 104: pp. 40–54. doi:10.1016/j.envsoft.2018.01.023.
  • Wang, X., Gao, X., Zhang, Y., Fei, X., Chen, Z., Wang, J., Zhang, Y., Lu, X., and Zhao, H. 2019. “Land-Cover classification of coastal wetlands using the RF algorithm for Worldview-2 and Landsat 8 images.” Remote Sensing, Vol. 11(No. 16): pp. 1927. doi:10.3390/rs11161927.
  • Watts, A.C., Ambrosia, V.G., and Hinkley, E.A. 2012. “Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use.” Remote Sensing, Vol. 4(No. 6): pp. 1671–1692. doi:10.3390/rs4061671.
  • Xiao, H., Shahab, A., Li, J., Xi, B., Sun, X., He, H., and Yu, G. 2019. “Distribution, ecological risk assessment and source identification of heavy metals in surface sediments of Huixian karst wetland, China.” Ecotoxicology and Environmental Safety, Vol. 185: pp. 109700. doi:10.1016/j.ecoenv.2019.109700.
  • Yu, L., and Gong, P. 2012. “Google Earth as a virtual globe tool for Earth science applications at the global scale: Progress and perspectives.” International Journal of Remote Sensing, Vol. 33(No. 12): pp. 3966–3986. doi:10.1080/01431161.2011.636081.
  • Zhao, L., Shi, Y., Liu, B., Hovis, C., Duan, Y., and Shi, Z. 2019. “Finer classification of crops by fusing UAV images and Sentinel-2A data.” Remote Sensing, Vol. 11(No. 24): pp. 3012. doi:10.3390/rs11243012.
  • Zhang, L., Gong, Z.N., Wang, Q.W., Jin, D.D., and Wang, X. 2019. “Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images.” Journal of Remote Sensing, Vol. 23: pp. 313–326.

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