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Classification of very high-resolution remote sensing images by applying a new edge-based marker-controlled watershed segmentation method

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Abstract

Recent advances in remote sensing technology have led to the creation of many sensors with very high spatial resolution, which enable researchers to detect and recognize earth surface features precisely in fine detail. There is an increasing demand for the development of algorithms to extract geometrical and spatial information from these kinds of images as the prerequisite for accurate thematic map generation. Among the existing techniques, segmentation-based methods are of great interest due to their capabilities in extracting spatial information of the image. In this article, a new watershed segmentation approach is proposed where the image edge information is exploited to improve the results. Next to its simplicity, the main characteristic of this newly proposed method is that there is no need to set any effective parameter(s), a common drawback confronted by most state-of-the-art segmentation techniques. The method is first applied to a well-known dataset, and the result of the segmentation phase is evaluated. The segmentation and classification phases of our approach are then both applied to selected Pleiades and WorldView-2 images. The final spatial–spectral classification map is compared to the results obtained through three common segmentation-based classification methods. The result of this comparison indicates that our approach outperforms the others by having 94.94% overall accuracy for a Pleiades image and 89.81% overall accuracy for a WorldView-2 image.

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References

  1. Gaetano, R., Masi, G., Poggi, G., Verdoliva, L., Scarpa, G.: Marker-controlled watershed-based segmentation of multiresolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 53(6), 2987–3004 (2015)

    Article  Google Scholar 

  2. Zhang, Q., Couloigner, I.: Automated road network extraction from high resolution multi-spectral imagery. In: ASPRS 2006 Annual Conference, Reno, Nevada 2006, pp. 1–10

  3. Bellens, R., Gautama, S., Martinez-Fonte, L., Philips, W., Chan, J.C.-W., Canters, F.: Improved classification of VHR images of urban areas using directional morphological profiles. IEEE Trans. Geosci. Remote Sens. 46(10), 2803–2813 (2008)

    Article  Google Scholar 

  4. Khurshid, H., Khan, M.F.: Classification of remotely sensed images using decimal coded morphological profiles. Signal Image Video Process. 10(6), 1001–1007 (2016)

    Article  Google Scholar 

  5. Dey, V., Zhang, Y., Zhong, M.: A review on image segmentation techniques with remote sensing perspective. In: ISPRS TC VII Symposium—100 Years ISPRS, Vienna, Austria

  6. Cigaroudy, L.S., Aghazadeh, N.: A multiphase segmentation method based on binary segmentation method for Gaussian noisy image. Signal Image Video Process. 11(5), 825–831 (2017)

    Article  Google Scholar 

  7. Korting, T.S., Dutra, L.V., Fonseca, L.M.G.: A resegmentation approach for detecting rectangular objects in high-resolution imagery. IEEE Geosci. Remote Sens. Lett. 8(4), 621–625 (2011)

    Article  Google Scholar 

  8. Hu, F., **a, G.-S., Hu, J., Zhang, L.: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7(11), 14680–14707 (2015)

    Article  Google Scholar 

  9. Zhang, L., Zhang, L., Du, B.: Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosci. Remote. Sens. Mag. 4(2), 22–40 (2016)

    Article  Google Scholar 

  10. Baatz, M., Schäpe, A.: Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte geographische informationsverarbeitung XII 58, 12–23 (2000)

    Google Scholar 

  11. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Article  Google Scholar 

  12. Pesaresi, M., Benediktsson, J.A.: A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans. Geosci. Remote Sens. 39(2), 309–320 (2001)

    Article  Google Scholar 

  13. Marques, O.: Practical Image and Video Processing Using MATLAB. Wiley, New York (2011)

    Book  Google Scholar 

  14. Gonzalez, W., Woods, R.E.: Eddins, Digital Image Processing Using MATLAB. Prentice Hall, Third New Jersey (2004)

    Google Scholar 

  15. Tarabalka, Y.: Classification of hyperspectral data using spectral-spatial approaches. Ph.D thesis, University of Iceland (2010)

  16. Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1558–1570 (2014)

    Article  Google Scholar 

  17. Lantuejoul, C.: La squelettisation et son application aux mesures topologiques des mosaiques polycristallines. (1978)

  18. Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection, workshop published. In: International Workshop on Image Processing CCETT/IRISA, Rennes, France (1979)

  19. Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, New York (2003)

    MATH  Google Scholar 

  20. Goutsias, J., Vincent, L., Bloomberg, D.S.: Mathematical Morphology and Its Applications to Image and Signal Processing, vol. 18. Springer, Berlin (2006)

    MATH  Google Scholar 

  21. Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit. 43(7), 2367–2379 (2010)

    Article  Google Scholar 

  22. Dollar, P., Tu, Z., Belongie, S.: Supervised learning of edges and object boundaries. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1964–1971 (2006)

  23. Lim, J., Zitnick, C., Dollár, P.: Sketch tokens: a learned mid-level representation for contour and object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3158–3165 (2013)

  24. Kontschieder, P., Rota Bulò, S., Bischof, H., Pelillo, M.: Structured class-labels in random forests for semantic image labelling. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2190–2197 (2011)

  25. Nowozin, S., Lampert, C.H.: Structured learning and prediction in computer vision. Found. Trends® Comput. Graph. Vis. 6(3–4), 185–365 (2011)

    MATH  Google Scholar 

  26. Mikes, S., Haindl, M., Scarpa, G., Gaetano, R.: Benchmarking of remote sensing segmentation methods. IEEE J. Sel. Top. Appl. Earth. Obs. Remote Sens. 8(5), 2240–2248 (2015)

    Article  Google Scholar 

  27. Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P.J., Bunke, H., Goldgof, D.B., Bowyer, K., Eggert, D.W., Fitzgibbon, A., Fisher, R.B.: An experimental comparison of range image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 673–689 (1996)

    Article  Google Scholar 

  28. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  29. Asvadi, A.: Multi-sensor object detection for autonomous driving. Ph.D. thesis in electronic and computer engineering, Universidade de Coimbra (2018)

  30. Asvadi, A., Karami, M., Baleghi, Y.: Efficient object tracking using optimized K-means segmentation and radial basis function neural networks. Int. J. Inf. Commun. Technol. Res. 4(1), 29–39 (2011)

    Google Scholar 

  31. Ghamisi, P., Couceiro, M.S., Fauvel, M., Benediktsson, J.A.: Integration of segmentation techniques for classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 11(1), 342–346 (2014)

    Article  Google Scholar 

  32. Michel, J., Youssefi, D., Grizonnet, M.: Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images. IEEE Trans. Geosci. Remote Sens. 53(2), 952–964 (2015)

    Article  Google Scholar 

  33. Liu, M.-Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097–2104

  34. Gaetano, R., Scarpa, G., Poggi, G.: Recursive texture fragmentation and reconstruction segmentation algorithm applied to VHR images. In: 2009 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. IV-101–IV-104 (2009)

  35. Scarpa, G., Masi, G., Gaetano, R., Verdoliva, L., Poggi, G.: Dynamic hierarchical segmentation of remote sensing images. In: International Conference on Image Analysis and Processing, pp. 371–380. Springer (2013)

  36. D’Elia, C., Poggi, G., Scarpa, G.: A tree-structured Markov random field model for Bayesian image segmentation. IEEE Trans. Image Process. 12(10), 1259–1273 (2003)

    Article  MathSciNet  Google Scholar 

  37. Michel, J., Grizonnet, M., Youssefi, D.: Mean shift based segmentation of full VHR imagery with limited resources: an exact solution. In: 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2842–2845 (2014)

  38. Pontius Jr., R.G., Millones, M.: Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 32(15), 4407–4429 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. K.N. Toosi University of Technology, Tehran, Iran

    Nafiseh Kakhani, Mehdi Mokhtarzade & Muhammad Javad Valadan Zouj

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  1. Nafiseh Kakhani
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  2. Mehdi Mokhtarzade
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  3. Muhammad Javad Valadan Zouj
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Correspondence to Nafiseh Kakhani.

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Kakhani, N., Mokhtarzade, M. & Valadan Zouj, M.J. Classification of very high-resolution remote sensing images by applying a new edge-based marker-controlled watershed segmentation method. SIViP 13, 1319–1327 (2019). https://doi.org/10.1007/s11760-019-01477-6

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