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Mixed Pixel Decomposition Based on Extended Fuzzy Clustering for Single Spectral Value Remote Sensing Images

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Abstract

The presence of mixed pixels in remote sensing images is the major issue for accurate classification. In this paper, we have focused on two aspects of mixed pixel problem: firstly, to identify mixed pixels from an image and secondly to label them to their appropriate class. In phase I, extraction of mixed pixels has been performed from the RSI images-based super-pixel algorithm and RGB model by using fuzzy C-means (FCM). In phase II, the extracted mixed pixel from phase I has been decomposed to the appropriate class. This new proposed technique is the amalgamation of PSO-FCM (particle swarm optimization-fuzzy C-means) for clustering of mixed pixels and ANN-BPO (artificial neural network-biogeography-based particle swarm optimization) for the classification purpose. Experimental results reveal that the proposed method has improved the accuracy as compared to the existing techniques and succeeds in better classification of the remote sensing images.

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References

  • Bhaskaran, S., Paramananda, S., & Ramnarayan, M. (2010). Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Applied Geography, 30(4), 650–665.

    Article  Google Scholar 

  • Bovolo, F., Bruzzone, L., & Carlin, L. (2010). A novel technique for subpixel image classification based on support vector machine. IEEE Transactions on Image Processing, 19(11), 2983–2999.

    Article  Google Scholar 

  • Campbell, J. B. (1987). Introduction into remote sensing. New York: The Guildford Press.

    Google Scholar 

  • Chaofan, W., Jinsong, D., Ke, W., Ligang, M., & Tahmassebi, A. R. S. (2016). Object-based classification approach for greenhouse mapping using Landsat-8 imagery. International Journal of Agricultural and Biological Engineering, 9(1), 79.

    Google Scholar 

  • Chen, Y., Ge, Y., Heuvelink, G. B., Hu, J., & Jiang, Y. (2015). Hybrid constraints of pure and mixed pixels for soft-then-hard super-resolution mapping with multiple shifted images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), 2040–2052.

    Article  Google Scholar 

  • Costa, H., Foody, G. M., & Boyd, D. S. (2017). Using mixed objects in the training of object-based image classifications. Remote Sensing of Environment, 190, 188–197.

    Article  Google Scholar 

  • Gudex-Cross, D., Pontius, J., & Adams, A. (2017). Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery. Remote Sensing of Environment, 196, 193–204.

    Article  Google Scholar 

  • Guerra, R., Santos, L., López, S., & Sarmiento, R. (2015). A new fast algorithm for linearly unmixing hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(12), 6752–6765.

    Article  Google Scholar 

  • Guidici, D., & Clark, M. L. (2017). One-Dimensional convolutional neural network land-cover classification of multi-seasonal hyperspectral imagery in the San Francisco Bay Area. California. Remote Sensing, 9(6), 629.

    Article  Google Scholar 

  • Hai, D. T., Son, H., & Vinh, L. T. (2017). Novel fuzzy clustering scheme for 3D wireless sensor networks. Applied Soft Computing, 54, 141–149.

    Article  Google Scholar 

  • Hansen, M. C. (2016). Classification trees and mixed pixel training data. In Remote sensing of land use and land cover (pp. 146–155). CRC Press.

  • Heinz, D. C. (2001). Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(3), 529–545.

    Article  Google Scholar 

  • Izakian, H., Abraham, A., & Snášel, V. (2009, December). Fuzzy clustering using hybrid fuzzy c-means and fuzzy particle swarm optimization. In World congress on nature & biologically inspired computing, 2009. NaBIC 2009 (pp. 1690–1694). IEEE.

  • Kaur, B., Sharma, M., Mittal, M., Verma, A., Goyal, L. M., & Hemanth, D. J. (2018). An improved salient object detection algorithm combining background and foreground connectivity for brain image analysis. Computers & Electrical Engineering, 71, 692–703.

    Article  Google Scholar 

  • Khodadadzadeh, M., Li, J., Plaza, A., Ghassemian, H., Bioucas-Dias, J. M., & Li, X. (2014). Spectral–spatial classification of hyperspectral data using local and global probabilities for mixed pixel characterization. IEEE Transactions on Geoscience and Remote Sensing, 52(10), 6298–6314.

    Article  Google Scholar 

  • Kumar, J. (2013). Image classification using SVM-RBF in the field of Image Processing. Image, 1(2), 12–17.

    Google Scholar 

  • Lillesand, Thomas M., & Kiefer, Ralph W. (1994). Remote sensing and photo interpretation. New York: Wiley.

    Google Scholar 

  • Long, H. V., Ali, M., Khan, M., & Tu, D. N. (2018). A novel approach for fuzzy clustering based on neutrosophic association matrix. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2018.11.007.

  • Marmanis, D., Datcu, M., Esch, T., & Stilla, U. (2016). Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geoscience and Remote Sensing Letters, 13(1), 105–109.

    Article  Google Scholar 

  • Mitraka, Z., Chrysoulakis, N., Kamarianakis, Y., Partsinevelos, P., & Tsouchlaraki, A. (2012). Improving the estimation of urban surface emissivity based on sub-pixel classification of high resolution satellite imagery. Remote Sensing of Environment, 117, 125–134.

    Article  Google Scholar 

  • Moody, A., Gopal, S., & Strahler, A. H. (1996). Artificial neural network response to mixed pixels in coarse-resolution satellite data. Remote Sensing of Environment, 58(3), 329–343.

    Article  Google Scholar 

  • Mori, G., Ren, X., Efros, A. A., & Malik, J. (2004). Recovering human body configurations: Combining segmentation and recognition. In Proceedings of the 2004 IEEE computer society conference on IEEE computer vision and pattern recognition, 2004. CVPR 2004 (Vol. 2, pp. II-326).

  • Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), 1145–1161.

    Article  Google Scholar 

  • Panchal, V. K., & Gupta, N. (2011, May). Swarm intelligence for mixed pixel resolution. In Proceedings of the 10th WSEAS international conference on telecommunications and informatics and microelectronics, nanoelectronics, optoelectronics, and WSEAS international conference on Signal processing (pp. 24–28). World Scientific and Engineering Academy and Society (WSEAS).

  • Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A. (2018). A new deep convolutional neural network for fast hyperspectral image classification. ISPRS journal of photogrammetry and remote sensing, 145, 120–147.

    Article  Google Scholar 

  • Phuong, P. T. M., Thong, P. H., & Son, L. H. (2018). Theoretical analysis of picture fuzzy clustering: Convergence and property. Journal of Computer Science and Cybernetics, 34(1), 17–32.

    Article  Google Scholar 

  • Raj, S. A., Nair, M. S., & Subrahmanyam, G. R. K. S. (2017). Satellite image resolution enhancement using nonsubsampled contourlet transform and clustering on subbands. Journal of the Indian Society of Remote Sensing, 45(6), 979–991.

    Article  Google Scholar 

  • Rollet, R., Benie, G. B., Li, W., Wang, S., & Boucher, J. M. (1998). Image classification algorithm based on the RBF neural network and K-means. International Journal of Remote Sensing, 19(15), 3003–3009.

    Article  Google Scholar 

  • Samat, A., Li, J., Liu, S., Du, P., Miao, Z., & Luo, J. (2016). Improved hyperspectral image classification by active learning using pre-designed mixed pixels. Pattern Recognition, 51, 43–58.

    Article  Google Scholar 

  • Sanpayao, M., Kasetkasem, T., Isshiki, T., Rakwatin, & P., Chanwimaluang, T. (2017). A subpixel classification algorithm based on a random forest approach. In 8th International conference of information and communication technology for embedded systems (IC-ICTES) (pp. 182–187).

  • Sinha, S., & Panchal, V. K. (2012). Resolving mixed pixels by hybridization of biogeography based optimization and ant colony optimization. In 2012 IEEE congress on evolutionary computation. IEEE (pp. 1–6).

  • Son, L. H. (2015). A novel kernel fuzzy clustering algorithm for geo-demographic analysis. Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, 317(C), 202–223.

    Google Scholar 

  • Son, L. H. (2016). Generalized picture distance measure and applications to picture fuzzy clustering. Applied Soft Computing, 46(C), 284–295.

    Article  Google Scholar 

  • Son, L. H., & Hai, P. V. (2016). A novel multiple fuzzy clustering method based on internal clustering validation measures with gradient descent. International Journal of Fuzzy Systems, 18(5), 894–903.

    Article  Google Scholar 

  • Son, L. H., & Thong, P. H. (2017). Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences. Applied Intelligence, 46(1), 1–15.

    Article  Google Scholar 

  • Son, L. H., & Tien, N. D. (2017). Tune up fuzzy C-means for big data: some novel hybrid clustering algorithms based on initial selection and incremental clustering. International Journal of Fuzzy Systems, 19(5), 1585–1602.

    Article  Google Scholar 

  • Son, L. H., & Tuan, T. M. (2016). A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation. Expert Systems with Applications, 46, 380–393.

    Article  Google Scholar 

  • Son, L. H., & Tuan, T. M. (2017). Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints. Engineering Applications of Artificial Intelligence, 59, 186–195.

    Article  Google Scholar 

  • Tam, N. T., Hai, D. T., Son, L. H., & Vinh, L. T. (2018). Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wireless Networks, 24(5), 1477–1490.

    Article  Google Scholar 

  • Thanh, N. D., Ali, M., & Son, L. H. (2017). A novel clustering algorithm in a neutrosophic recommender system for medical diagnosis. Cognitive Computation, 9(4), 526–544.

    Article  Google Scholar 

  • Thong, P. H., & Son, L. H. (2016a). Picture fuzzy clustering for complex data. Engineering Applications of Artificial Intelligence, 56, 121–130.

    Article  Google Scholar 

  • Thong, P. H., & Son, L. H. (2016b). A novel automatic picture fuzzy clustering method based on particle swarm optimization and picture composite cardinality. Knowledge-Based Systems, 109, 48–60.

    Article  Google Scholar 

  • Thong, P. H., & Son, L. H. (2016c). Picture fuzzy clustering: a new computational intelligence method. Soft Computing, 20(9), 3549–3562.

    Article  Google Scholar 

  • Tits, L., Somers, B., Saeys, W., & Coppin, P. (2014). Site-specific plant condition monitoring through hyperspectral alternating least squares unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3606–3618.

    Article  Google Scholar 

  • Tuan, T. M., Ngan, T. T., & Son, L. H. (2016). A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental X-ray image segmentation. Applied Intelligence, 45(2), 402–428.

    Article  Google Scholar 

  • Villa, A., Chanussot, J., Benediktsson, J. A., Jutten, C., & Dambreville, R. (2013). Unsupervised methods for the classification of hyperspectral images with low spatial resolution. Pattern Recognition, 46(6), 1556–1568.

    Article  Google Scholar 

  • Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), 884–893.

    Article  Google Scholar 

  • Yuan, Y., Fu, M., & Lu, X. (2015). Substance dependence constrained sparse NMF for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 53(6), 2975–2986.

    Article  Google Scholar 

  • Zhong, Y., Wang, X., Zhao, L., Feng, R., Zhang, L., & Xu, Y. (2016). Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 49–63.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Gurukashi University, Bathinda, Punjab, India

    Sumit Kaur & R. K. Bansal

  2. Department of Computer Science and Engineering, G.B. Pant Govt. Engineering College, Okhla, New Delhi, India

    Mamta Mittal

  3. Department of Computer Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, India

    Lalit Mohan Goyal

  4. Department of Computer Science and Engineering, Chandigarh Group of Colleges, Landran, Punjab, India

    Iqbaldeep Kaur & Amit Verma

  5. VNU Information Technology Institute, Vietnam National University, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam

    Le Hoang Son

Authors
  1. Sumit Kaur
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  2. R. K. Bansal
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  4. Lalit Mohan Goyal
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  5. Iqbaldeep Kaur
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  7. Le Hoang Son
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Corresponding author

Correspondence to Le Hoang Son.

Appendix

Appendix

See Table 9.

Table 9 Summary of existing algorithms for end member extraction
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Kaur, S., Bansal, R.K., Mittal, M. et al. Mixed Pixel Decomposition Based on Extended Fuzzy Clustering for Single Spectral Value Remote Sensing Images. J Indian Soc Remote Sens 47, 427–437 (2019). https://doi.org/10.1007/s12524-019-00946-2

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  • DOI: https://doi.org/10.1007/s12524-019-00946-2

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