Abstract
Supplementary information, such as multi-temporal spectral data and textural features, has the potential to improve land cover classification accuracy. However, given the larger volumes of remote sensing data, it is difficult to utilize all the features of remote sensing big data having different times and spatial resolutions. Inefficiency is also a large problem when dealing with large area land cover mapping. In this study, a new mode of incorporating spatial and temporal dependencies in a complex region employing the random forests (RFs) classifier was utilized. To map land covers, spring and autumn spectral images and their spectral indexes, textural features obtained from Landsat 5 were selected, and an importance measure variable was used to reduce the data’s dimension. In addition to randomly selecting the variable, we used random sampling to furthest decrease the generalization error in RF. The results showed that utilizing random sampling, multi-temporal spectral image and texture features, the classification of the Wuhan urban agglomeration, China, using RF performed well. The RF algorithm yielded an overall accuracy of 89.2% and a Kappa statistic of 0.8522, indicating high model performance. In addition, the variable importance measures demonstrated that the type of textural features was extremely important for intra-class separability. The RF model has transitivity. The algorithm can be extended by choosing a set of appropriate features for signature extension over large areas or in time-series of Landsat imagery. Land cover mapping might be more economical and efficient if no-cost imagery is used.
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References
Roscher, R., Waske, B.: Shapelet-based sparse image representation for landcover classification of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 54, 1623–1634 (2016)
Lane, C.R., Liu, H.X., Autrey, B.C., Anenkhonov, O.A., Chepinoga, V.V., Wu, Q.S.: Improved wetland classification using eight-band high resolution satellite imagery and a hybrid approach. Remote Sens. 6, 12187–12216 (2014)
Zheng, H.B., Cheng, T., Yao, X., Deng, X.Q., Tian, Y.C., Cao, W.X., Zhu, Y.: Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Res. 198, 131–139 (2016)
Tatsumi, K., Yamashiki, Y., Torres, M.A.C., Taipe, C.L.R.: Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data. Comput. Electron. Agric. 115, 171–179 (2015)
Zhang, X.M., He, G.J., Wang, M.M., Zhang, Z.M., Jiao, W.L., Peng, Y., Wang, G.Z., Liu, H.C., Long, T.F.: Eco-environmental assessment and analysis of Tonglvshan mining area in Daye City, Hubei Province based on spatiotemporal methodology. In: 2015 International Workshop on Spatiotemporal Computing. ISPRS Annals of the Photogrammetry, Fairfax, VA, USA, pp. 211–215 (2015)
Khatami, R., Mountrakis, G., Stehman, V.S.: A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sens. Environ. 177, 89–100 (2016)
Tsaneva, M.G., Krezhova, D.D., Yanev, T.K.: Development and testing of a statistical texture model for land cover classification of the Black Sea region with MODIS imagery. Adv. Space Res. 46, 872–878 (2010)
Agüera, F., Aguilar, F.J., Aguilar, M.A.: Using texture analysis to improve perpixel classification of very high resolution images for mapping plastic greenhouses. ISPRS J. Photogramm. Remote Sens. 63, 635–646 (2008)
Asner, G.P., Keller, M., Pereira Jr., R., Zweede, J.C.: Remote sensing of selective logging in Amazonia: assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis. Remote Sens. Environ. 80, 483–496 (2002)
Chica-Olmo, M., Abarca-Hernández, F.: Computing geostatistical image texture for remotely sensed data classification. Comput. Geosci. 26, 373–383 (2000)
Franklin, S.E., Hall, R.J., Moskal, L.M., Maudie, A.J., Lavigne, M.B.: Incorporating texture into classification of forest species composition from airborne multispectral images. Int. J. Remote Sens. 21, 61–79 (2000)
Ghimire, B., Rogan, J., Miller, J.: Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic. Remote Sens. Lett. 1, 45–54 (2010)
Hayes, M.M., Miller, S.N., Murphy, M.A.: High-resolution landcover classification using Random Forest. Remote Sens. Lett. 5, 112–121 (2014)
Berthelot, A., Solberg, A., Gelius, L.J.: Texture attributes for detection of salt. J. Appl. Geophys. 88, 52–69 (2013)
Ghosh, A., Joshi, P.K.: A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution Worldview 2 imagery. Int. J. Appl. Earth Obs. Geoinf. 26, 298–311 (2014)
Eitzel, M.V., Kelly, M., Dronova, I., Valachovic, Y., Quinn-Davidson, L., Solera, J., Valpine, P.: Challenges and opportunities in synthesizing historical geospatial data using statistical models. Ecol. Inform. 31, 100–111 (2016)
Wang, L., Song, W., Liu, P.: Link the remote sensing big data to the image features via wavelet transformation. Cluster Comput. Arch. 19(2), 793–810 (2016)
Li, X., Wang, L.: On the study of fusion techniques for bad geological remote sensing image. J. Ambient Intell. Humaniz. Comput. 6, 141–149 (2015)
Huang, K., Ruimin, H., Jiang, J., Han, Z., Wang, F.: HRM graph constrained dictionary learning for face image super-resolution. Multimed. Tools Appl. 76(2), 3139–3162 (2017)
Wang, L., Ke, L., Liu, P., Ranjan, R., Chen, L.: IK-SVD: dictionary learning for spatial big data via incremental atom update. Comput. Sci. Eng. 16(4), 41–52 (2014)
Wang, L., Zhang, J., Liu, P., Choo, K.-K.R., Huang, F.: Spectral-spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Comput. 21(1), 213–221 (2017). doi:10.1007/s00500-016-2246-3
Yang, C., Yu, M., Hu, F., Jiang, Y., Li, Y.: Utilizing Cloud Computing to address big geospatial data challenges. Comput. Environ. Urban Syst. 61, 120–128 (2017)
Pal, M.: Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26, 217–222 (2005)
Gislason, P.O., Benediktsson, J.A., Sveinsson, J.R.: Random Forests for land cover classification. Pattern Recogn. Lett. 27, 294–300 (2006)
Adam, E., Mutanga, O., Odindi, J., Abdel-Rahman, E.M.: Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. Int. J. Remote Sens. 35, 3440–3458 (2014)
Zhu, Z., Gallant, A., Woodcock, C., Pengra, B., Olofsson, P., Loveland, T., Jin, S., Dahal, D., Yang, L., Auch, R.: Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative. ISPRS J. Photogramm. Remote Sens. 122, 206–221 (2016)
Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Silleos, N.G., Alexandridis, T.K., Gitas, I.Z., Perakis, K.: Vegetation indices: advances made in biomass estimation and vegetation monitoring in the last 30 years. Geocarto Int. 21, 21–28 (2006)
Rouse, J., Haas, R., Schell, J., Deering, D.: Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASA, pp. 309–317 (1973)
Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B.: Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens. Environ. 90, 337–352 (2004)
Vescovo, L., Gianelle, D.: Using the MIR bands in vegetation indices for the estimation of grassland biophysical parameters from satellite remote sensing in the Alps region of Trentino (Italy). Adv. Space Res. 41, 1764–1772 (2008)
Jiang, Z., Huete, A.R., Chen, J., Chen, Y., Li, J., Yanc, G., Zhang, X.: Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens. Environ. 101(3), 366–378 (2006)
DeFries, R., Hansen, M., Townshend, J.R.G.: Global discrimination of land cover from metrics derived from AVHRR Pathfinder-data sets. Remote Sens. Environ. 54, 209–222 (1995)
Liu, H., Huete, A.R.: A feedback based modification of the NDVI to minimize canopy back ground and atmospheric noise. IEEE Trans. Geosci. Remote Sens. 33, 457–465 (1995)
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002)
Xu, H.: Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025–3033 (2006)
Pelletiera, C., Valeroa, S., Ingladaa, J., Championb, N., Dedieua, G.: Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Remote Sens. Environ. 187, 156–168 (2016)
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)
Beekhuizen, J., Clarke, K.C.: Toward accountable land use mapping: using geocomputation to improve classification accuracy and reveal uncertainty. Int. J. Appl. Earth Obs. Geoinf. 12, 127–137 (2010)
Pacifici, F., Chini, M., Emery, W.J.: A neural network approach using multiscale textural metrics from very high-resolution panchromatic imagery for urban land-use classification. Remote Sens. Environ. 113, 1276–1292 (2009)
Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach. Learn. 40, 139–157 (2000)
Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., Rigol-Sanchez, J.P.: An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 67, 93–104 (2012)
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37–46 (1960)
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This research has been supported by the National Research Program on Global Changes and Adaptation: Rapid production method of large scale global change products (2016YFA0600302) and by National Ecological Environment Change Assessment by Remote Sensing Survey Project 2000–2010 (STSN-10-03) Grants.
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Zhang, X.M., He, G.J., Zhang, Z.M. et al. Spectral-spatial multi-feature classification of remote sensing big data based on a random forest classifier for land cover mapping. Cluster Comput 20, 2311–2321 (2017). https://doi.org/10.1007/s10586-017-0950-0
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DOI: https://doi.org/10.1007/s10586-017-0950-0