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Potential of Multi-scale Completed Local Binary Pattern for Object Based Classification of Very High Spatial Resolution Imagery

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Abstract

This paper explores the potentiality of using the completed local binary pattern (CLBP) for the classification of an urbanized oasis area situated in southeastern Tunisia, in very high spatial resolution GeoEye imagery. To further improve the spatial information description derived by CLBP, which is successfully used in face recognition, we applied the multi-scale completed local binary pattern (MSCLBP) to classify the single-dated image. A supervised classification framework preceded by mean-shift segmentation is applied using the texture features alone and combined with the normalized difference vegetation index (NDVI). As the segmentation is a crucial step to obtain a good mapping, considerations are given to select the optimal combination of mean-shift input parameters, such as spatial radius and range radius. The results of this study indicate that MSCLBP outperforms the single-scale CLBP and the gray-level co-occurrence matrix (GLCM) descriptors in the task of expressing the classes of interests.

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References

  • Ahonen, T., Hadid, A., & Pietikäinen, M. (2004). Face recognition with local binary patterns. European conference on computer vision. Springer, Berlin, Heidelberg. 469–481. https://doi.org/10.1007/978-3-540-24670-1_36.

  • Bagchi, M. J., Mohanty, F., Rup, S., Dash, B., & Majhi, B. (2018). Digital mammogram classification using compound local binary pattern features with principal component analysis based feature reduction approach. International conference on advances in computing and data sciences. 270–278. Springer, Singapore.

  • Balaguer, A., Ruiz, L. A., Hermosilla, T., & Recio, J. A. (2010). Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification. Computers & Geosciences, 36, 231–240. https://doi.org/10.1016/j.cageo.2009.05.003.

    Article  Google Scholar 

  • Beguet, B., Boukir, S., Guyon, D., & Chehata, N. (2013). Modelling-based feature selection for classification of forest structure using very high resolution multispectral imagery. In: 2013 IEEE international conference on systems, man, and cybernetics. 4294–4299. https://doi.org/10.1109/SMC.2013.732.

  • Beguet, B., Chehata, N., Boukir, S., & Guyon, D. (2012). Retrieving forest structure variables from very high resolution satellite images using an automatic method. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. https://doi.org/10.5194/isprsannals-I-7-1-2012.

    Article  Google Scholar 

  • Ben Salah, M. (2011). La palmeraie de Gabès. Phoenix Project.

  • Ben Salem, Y., & Nasri, S. (2009). Automatic classification of woven fabrics using multi-class support vector machine. Research Journal of Textile and Apparel, 13(2), 28–36. https://doi.org/10.1108/RJTA-13-02-2009-B004.

    Article  Google Scholar 

  • Boschi, L. S., & Galo, M. D. L. B. T. (2007). Uncertainty analysis in the representation of the urban land cover classes through the application of artificial neural network. Boletim de Ciências Geodésicas, 13, 22–41.

    Google Scholar 

  • Bruzzone, L., Roli, F., & Serpico, S. B. (1995). An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection. IEEE Transactions on Geoscience and Remote Sensing, 33, 1318–1321.

    Article  Google Scholar 

  • Camlica, Z., Tizhoosh, H. R., & Khalvati, F. (2015). Medical image classification via SVM using LBP features from saliency-based folded data. In: 2015 IEEE 14th international conference on machine learning and applications (ICMLA). 128–132. https://doi.org/10.1109/ICMLA.2015.131.

  • Carpentier, I., & Gana, A. (2017). Changing agricultural practices in the Oasis of southern Tunisia: Conflict and competition for resources in a post-revolutionary and globalization context. Oases and Globalization. https://doi.org/10.1007/978-3-319-50749-1.

    Article  Google Scholar 

  • Chabrier, S., Emile, B., Rosenberger, C., & Laurent, H. (2006). Unsupervised performance evaluation of image segmentation. EURASIP Journal on Applied Signal Processing, 2006, 217–217. https://doi.org/10.1155/ASP/2006/96306.

    Article  Google Scholar 

  • Chabrier, S., Rosenberger, C., Laurent, H., Emile, B., & Marché, P. (2004). Evaluating the segmentation result of a gray-level image. In: 2004 12th European signal processing conference, 953–956. IEEE.

  • Champion, I., Germain, C., Da Costa, J. P., Alborini, A., & Dubois-Fernandez, P. (2013). Retrieval of forest stand age from SAR image texture for varying distance and orientation values of the gray level co-occurrence matrix. IEEE Geoscience and Remote Sensing Letters, 11, 5–9. https://doi.org/10.1109/lgrs.2013.2244060.

    Article  Google Scholar 

  • Chen, Z., Pasher, J., Duffe, J., & Behnamian, A. (2017). Mapping arctic coastal ecosystems with high resolution optical satellite imagery using a hybrid classification approach. Canadian Journal of Remote Sensing., 43, 513–527. https://doi.org/10.1080/07038992.2017.1370367.

    Article  Google Scholar 

  • Coburn, C. A., & Roberts, A. C. B. (2004). A multiscale texture analysis procedure for improved forest stand classification. International Journal of Remote Sensing, 25, 4287–4308. https://doi.org/10.1080/0143116042000192367.

    Article  Google Scholar 

  • Comaniciu, D., & Meer, P. (1999). Mean shift analysis and applications. In: Proceedings of the seventh IEEE international conference on computer vision. 1197–1203. IEEE. https://doi.org/10.1109/iccv.1999.790416.

  • Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis & Machine Intelligence., 24, 603–619.

    Article  Google Scholar 

  • Delenne, C., Durrieu, S., Rabatel, G., Deshayes, M., Bailly, J. S., Lelong, C., & Couteron, P. (2008). Textural approaches for vineyard detection and characterization using very high spatial resolution remote sensing data. International Journal of Remote Sensing., 29, 1153–1167. https://doi.org/10.1080/01431160701311259.

    Article  Google Scholar 

  • Doshi, N. P. (2014). Multi-dimensional local binary pattern texture descriptors and their application for medical image analysis. Doctoral dissertation, © Niraj P. Doshi.

  • Dubey, S. R., and Jalal, A. S. (2012). Detection and classification of apple fruit diseases using complete local binary patterns. In: Proceedings of the 3rd international conference on computer and communication technology. 346–351.

  • Ferro, C. J. S., & Warner, T. A. (2002). Scale and texture in digital image classification. Photogrammetric Engineering & Remote Sensing., 68, 51–63.

    Google Scholar 

  • Franklin, S. E., Hall, R. J., Moskal, L. M., Maudie, A. J., & Lavigne, M. B. (2000). Incorporating texture into classification of forest species composition from airborne multispectral images. International journal of remote sensing, 21, 61–79. https://doi.org/10.1080/014311600210993.

    Article  Google Scholar 

  • Grizonnet, M., Michel, J., Poughon, V., et al. (2017). Orfeo Toolbox: Open source processing of remote sensing images. Open Geospatial Data, Software and Standards, 2, 15. https://doi.org/10.1186/s40965-017-0031-6.

    Article  Google Scholar 

  • Guo, Z., Zhang, L., & Zhang, D. (2010). A completed modeling of local binary pattern operator for texture classification. IEEE transactions on image processing, 19, 1657–1663. https://doi.org/10.1109/TIP.2010.2044957.

    Article  Google Scholar 

  • Haralick, R. M., (1971). On a texture-context feature extraction algorithm for remotely sensed imagery. In: 1971 IEEE conference on decision and control. 650–657. https://doi.org/10.1109/cdc.1971.271084.

  • Haralick, R. M., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, man and Cybernetics. https://doi.org/10.1109/TSMC.1973.4309314.

    Article  Google Scholar 

  • Huang, X., & Zhang, L. (2008). An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 46, 4173–4185.

    Article  Google Scholar 

  • Karoui, I., Fablet, R., Boucher, J. M., & Augustin, J. M. (2008). Segmentation texturales des images sonar des fonds marins par une approche variationnelle basée région. Traitement du signal, 25, 73–85.

    Google Scholar 

  • Longbotham, N., Pacifici, F., Glenn, T., Zare, A., Volpi, M., Tuia, D., et al. (2012). Multi-modal change detection, application to the detection of flooded areas: Outcome of the 2009–2010 data fusion contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 331–342. https://doi.org/10.1109/JSTARS.2011.2179638.

    Article  Google Scholar 

  • Liu, Y., Fan, B., Wang, L., Bai, J., Xiang, S., & Pan, C. (2018). Semantic labeling in very high resolution images via a self-cascaded convolutional neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 78–95.

    Article  Google Scholar 

  • Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., & Zhang, Y. (2018). Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sensing, 10, 1119.

    Article  Google Scholar 

  • Mu, Y., Yan, S., Liu, Y., Huang, T., & Zhou, B. (2008). Discriminative local binary patterns for human detection in personal album. In: 2008 IEEE conference on computer vision and pattern recognition. 1–8. https://doi.org/10.1109/CVPR.2008.4587800.

  • Musci, M., Feitosa, R. Q., Velloso, M. L. F., Novack, T., & da Costa, G. A. O. P. (2011). An evaluation of texture descriptors based on local binary patterns for classifications of remote sensing images. Boletim de Ciências Geodésicas, 17, 549–570.

    Article  Google Scholar 

  • Nasonova, S., Scharien, R. K., Geldsetzer, T., Howell, S. E. L., & Power, D. (2018). Optimal compact polarimetric parameters and texture features for discriminating sea ice types during winter and advanced melt. Canadian Journal of Remote Sensing, 44, 390–411. https://doi.org/10.1080/07038992.2018.1527683.

    Article  Google Scholar 

  • Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29, 51–59. https://doi.org/10.1016/0031-3203(95)00067-4.

    Article  Google Scholar 

  • Ojala, T., Pietikäinen, M., and Mäenpää, T. (2001). A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. In: International conference on advances in pattern recognition, 399–408.

  • Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 224, 971–987.

    Article  Google Scholar 

  • Ouma, Y. O., Tateishi, R., & Sri-Sumantyo, J. T. (2010). Urban features recognition and extraction from very-high resolution multi-spectral satellite imagery: A micro-macro texture determination and integration framework. IET Image Processing, 4, 235–254. https://doi.org/10.1049/iet-ipr.2007.

    Article  Google Scholar 

  • Ouma, Y. O., Tetuko, J., & Tateishi, R. (2008). Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very-high-resolution optical-sensor imagery. International Journal of Remote Sensing., 29, 3417–3456. https://doi.org/10.1080/01431160701601782.

    Article  Google Scholar 

  • Pacifici, F., Chini, M., & Emery, W. J. (2009). A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification. Remote Sensing of Environment, 113, 1276–1292. https://doi.org/10.1016/j.rse.2009.02.014.

    Article  Google Scholar 

  • Parikesit, S. O., Muhamad, D., Harashina, K., Takeuchi, K., & Umezaki, M. (2010). Land use/cover classification of a complex agricultural landscape using single-dated very high spatial resolution satellite-sensed imagery. Canadian Journal of Remote Sensing, 36, 722–736. https://doi.org/10.5589/m11-010.

    Article  Google Scholar 

  • Pesaresi, M., Gerhardinger, A., & Kayitakire, F. (2008). A robust built-up area presence index by anisotropic rotation-invariant textural measure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1, 180–192. https://doi.org/10.1109/JSTARS.2008.2002869.

    Article  Google Scholar 

  • Pipaud, I., & Lehmkuhl, F. (2017). Object-based delineation and classification of alluvial fans by application of mean-shift segmentation and support vector machines. Geomorphology, 293, 178–200. https://doi.org/10.1016/j.geomorph.2017.05.013.

    Article  Google Scholar 

  • Rao, P. V. N., Sesha Sai, M. V. R., Sreenivas, K., Rao, M. V. K., Rao, B. R. M., Dwivedi, R. S., & Venkataratnam, L. (2002). Textural analysis of IRS-1D panchromatic data for land cover classification. International Journal of Remote Sensing, 23, 3327–3345. https://doi.org/10.1080/01431160110104665.

    Article  Google Scholar 

  • Regniers, O., Bombrun, L., Lafon, V., & Germain, C. (2016). Supervised classification of very high resolution optical images using wavelet-based textural features. IEEE Transactions on Geoscience and Remote Sensing, 54, 3722–3735.

    Article  Google Scholar 

  • Regniers, O., Member, S., Bombrun, L., Guyon, D., & Germain, C. (2015). Wavelet-based texture features for the classification of age classes in a maritime pine. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2014.2353656.

    Article  Google Scholar 

  • Rosenberger, C. (1999). Mise en oeuvre d’un système adaptatif de segmentation d’images.Doctoral dissertation.

  • Sghaier, M. O., and Lepage, R. (2015). Change detection using multiscale segmentation and Kullback-Leibler divergence: Application on road damage extraction. In: 2015 8th international workshop on the analysis of multitemporal remote sensing images (Multi-Temp), 1–4. https://doi.org/10.1109/Multi-Temp.2015.7245765.

  • Shan, C., Gong, S., & McOwan, P. W. (2009). Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing, 27, 803–816. https://doi.org/10.1016/j.imavis.2008.08.005.

    Article  Google Scholar 

  • Singh, S., Maurya, R., and Mittal, A. (2012). Application of complete local binary pattern method for facial expression recognition. In: 2012 4th international conference on intelligent human computer interaction (IHCI).1–4. https://doi.org/10.1109/IHCI.2012.6481801.

  • Wang, M., Fei, X., Zhang, Y., Chen, Z., Wang, X., Tsou, J. Y., et al. (2018). Assessing texture features to classify coastal wetland vegetation from high spatial resolution imagery using completed local binary patterns (CLBP). Remote Sensing, 10, 778. https://doi.org/10.3390/rs10050778.

    Article  Google Scholar 

  • Warner, T. A., & Steinmaus, K. (2005). Spatial classification of orchards and vineyards with high spatial resolution panchromatic imagery. Photogrammetric Engineering Remote Sensing, 71, 179–187. https://doi.org/10.14358/pers.71.2.179.

    Article  Google Scholar 

  • Zhang, R., & Zhu, D. (2011). Study of land cover classification based on knowledge rules using high-resolution remote sensing images. Expert Systems with Applications, 38, 3647–3652. https://doi.org/10.1016/j.eswa.2010.09.019.

    Article  Google Scholar 

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Authors and Affiliations

  1. Research Laboratory Modeling, Analysis and Control of Systems (MACS), National Engineering School of Gabes (ENIG), University of Gabes, Gabes, Tunisia

    Radhia Chairet, Yassine Ben Salem & Mohamed Aoun

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  1. Radhia Chairet
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  2. Yassine Ben Salem
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  3. Mohamed Aoun
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Correspondence to Radhia Chairet.

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Chairet, R., Ben Salem, Y. & Aoun, M. Potential of Multi-scale Completed Local Binary Pattern for Object Based Classification of Very High Spatial Resolution Imagery. J Indian Soc Remote Sens 49, 1245–1255 (2021). https://doi.org/10.1007/s12524-021-01311-y

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