[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 12, Issue 2 (1-2023) ::
JGST 2023, 12(2): 98-113 Back to browse issues page
Spectral-spatial classification of hyperspectral images by combining hierarchical and marker-based Minimum Spanning Forest algorithms
Davood Aakbari *, Komeil Rokni
Abstract:   (322 Views)
Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. This study proposes a modified spectral–spatial classification approach for improving the spectral–spatial classification of hyperspectral images. In the proposed method ten spatial/texture features, using mean, standard deviation, contrast, homogeneity, correlation, dissimilarity, energy, entropy, wavelet transform and Gabor filter, are first extracted. The Weighted Genetic algorithm is then used to obtain the subspace of hyperspectral data and texture features. Finally, the hierarchical segmentation and marker-based Minimum Spanning Forest (MSF) classification algorithms are combined with the majority voting law. To evaluate the efficiency of the proposed approach two image datasets, Indiana Pine and Washington DC Mall, were used. Experimental results demonstrate that the proposed approach achieves approximately 10% and 7% better overall accuracy than the Support Vector Machine (SVM) algorithm for these datasets, respectively.
Article number: 7
Keywords: Hyperspectral imagery, Spectral-spatial classification, Spatial features, Weighted genetic, Hierarchical segmentation, Marker-based MSF
Full-Text [PDF 429 kb]   (122 Downloads)    
Type of Study: Research | Subject: Photo&RS
1. P. K. Varshney, and M. K. Arora, "Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data", Springer Berlin Heidelberg New York, 2004. [DOI:10.1007/978-3-662-05605-9]
2. C. I. Chang, Hyperspectral Imaging: Techniques for spectral Detection and Classification. Orlando, FL: Kluwer Academic, 2003.
3. R. H. Chan, K. K. Kan, M. Nikolova, and R. J. Plemmons, "A two-stage method for spectral-spatial classification of hyperspectral images", J. Math Imaging Vis., Vol. 62, pp. 790-807, 2020. [DOI:10.1007/s10851-019-00925-9]
4. R. C. Gonzalez, and R. E. Woods, Digital Image Processing. Prentice Hall, pp. 617 - 626, 2002.
5. J. Acquarelli, E. Marchiori, L. M. C. Buydens, T. Tran, and T. V. Laarhoven, "Spectral-spatial classification of hyperspectral images: three tricks and a new learning setting", Remote Sens., Vol. 10, pp. 1156, 2018. [DOI:10.3390/rs10071156]
6. H. Hasani, F. Samadzadegan, and P. Reinartz, "A metaheuristic feature-level fusion strategy in classification of urban area using hyperspectral imagery and LiDAR data", European Journal of Remote Sensing, Vol. 50., No. 1, pp. 222-236, 2017. [DOI:10.1080/22797254.2017.1314179]
7. V. Vapnik, The Nature of Statistical Learning Theory. New York, NY: Springer-Verlag, 1995. [DOI:10.1007/978-1-4757-2440-0]
8. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers", IEEE Trans. Syst., Man, Cybern. B, Cybern., Vol. 40, pp. 1267-1279, 2010. [DOI:10.1109/TSMCB.2009.2037132]
9. M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, "Advances in Spectral-Spatial Classification of Hyperspectral Images", Proceedings of the IEEE, Vol. 101, No. 3, pp. 652-675, 2013. [DOI:10.1109/JPROC.2012.2197589]
10. E. Pan, X. Mei, Q. Wang, Y. Ma, and J. Ma, "Spectral-spatial classification for hyperspectral image based on a single GRU", Neurocomputing, Vol. 387, pp. 150-160, 2020. [DOI:10.1016/j.neucom.2020.01.029]
11. D. Hong, X. Wu, P. Ghamisi, J. Chanussot, N. Yokoya, X. X. Zhu, "Invariant attribute profiles: a spatial-frequency joint feature extractor for hyperspectral image classification", IEEE Trans. Geosci. Remote Sens., Vol. 58, pp.3791-3808, 2020. [DOI:10.1109/TGRS.2019.2957251]
12. J. A. Benediktsson, M. Pesaresi, and K. Arnason, "Classification and feature extraction for remote sensing images from urban areas based on morphological transformations", IEEE Trans. Geos. and Remote Sens., Vol. 41, No. 9, pp. 1940-1949, 2003. [DOI:10.1109/TGRS.2003.814625]
13. M. Pesaresi, and J. A. Benediktsson, "A new approach for the morphological segmentation of high-resolution satellite imagery", IEEE Trans. Geosci. Remote Sens., Vol. 39, No. 2, pp. 309-320, 2001. [DOI:10.1109/36.905239]
14. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles", IEEE Trans. Geos. and Remote Sens., Vol. 43, No. 3, pp. 480-491, 2005. [DOI:10.1109/TGRS.2004.842478]
15. X. Huang, and L. Zhang, "A comparative study of spatial approaches for urban mapping using hyperspectral rosis images over pavia city, northern Italy", International Journal of Remote Sensing, Vol. 30, No. 12, pp. 3205-3221, 2009. [DOI:10.1080/01431160802559046]
16. Y. Tarabalka, J. C. Tilton, J. A. Benediktsson, and J. Chanussot, "A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011. [DOI:10.1109/JSTARS.2011.2173466]
17. J. Tilton, "Analysis of hierarchically related image segmentations", in Proc. IEEE Workshop Adv. Tech. Anal. Remotely Sensed Data, pp. 60-69, 2003.
18. P. Soille, Morphological Image Analysis. 2nd ed. Berlin, Germany: Springer-Verlag, 2003. [DOI:10.1007/978-3-662-05088-0]
19. O. Gómez, J. A. González, and E. F. Morales, "Image segmentation using automatic seeded region growing and instance-based learning", in Proc. 12th Iberoamerican Congress Pattern Recognition, Valparaiso, Chile, pp. 192-201, 2007. [DOI:10.1007/978-3-540-76725-1_21]
20. G. Noyel, "Filtrage, Réduction de Dimension, Classification et Segmentation Morphologique Hyperspectrale", Ph.D. dissertation, Ctr. Mathematical Morphology, Paris Sch. Mines, Paris, France, 2008.
21. G. Noyel, J. Angulo, and D. Jeulin, "Morphological segmentation of hyperspectral images", Image Anal. Stereol., Vol. 26, pp. 101-109, 2007. [DOI:10.5566/ias.v26.p101-109]
22. R. D. Silva, and H. Pedrini, "Hyperspectral Data Classification Improved by Minimum Spanning Forests," IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 7, 2015.
23. D. Akbari, A. Safari, S. Khazai, and S. Homayouni. "Improved Spectral-Spatial Classification Minimum Spanning Forest by Reducing the Spatial Dimensions of Hyperspectral Images", JGST., Vol. 5, No. 2, pp. 219-229, 2015 (in Persian).
24. D. Akbari, A. Safari, and S. Khazai, "The effect of feature selection using genetic algorithms on spectral-spatial classification of hyperspectral imagery", jgit., Vol. 3, No. 1, pp. 45-60, 2015, (in Persian). [DOI:10.29252/jgit.3.1.45]
25. M. Golipour, H. Ghassemian, and F. Mirzapour, "Integrating Hierarchical Segmentation Maps With MRF Prior for Classification of Hyperspectral Images in a Bayesian Framework," IEEE Transactions on Geoscience and Remote Sensing, 2015. [DOI:10.1109/TGRS.2015.2466657]
26. D. Akbari, A. Safari, and S. Homayouni, "Improving the spectral-spatial classification of hyperspectral images using spatial information in selecting markers", Scientific-Research Quarterly of Geographical Data (SEPEHR), Vol. 25, No. 98, pp. 5-14, 2016, (in Persian).
27. D. Akbari, "Improving Spectral-spatial Classification of Hyperspectral Imagery Using Spectral Dimensionality Reduction Based on Weighted Genetic Algorithm", J. Indian Soc. Remote Sens., Vol. 45, No. 6, pp. 927-937, 2017. [DOI:10.1007/s12524-016-0652-8]
28. S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi, J. A. Benediktsson, "Deep Learning for Hyperspectral Image Classification: An Overview", IEEE Transactions on Geoscience and Remote Sensing, pp. 1-20, 2019. [DOI:10.1109/TGRS.2019.2907932]
29. D. Akbari, "A novel method for spectral-spatial classification of hyperspectral images with a high spatial resolution", Arabian Journal of Geosciences, Vol. 13, pp. 1-10, 2020. [DOI:10.1007/s12517-020-06289-4]
30. R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural Features for Image Classification", IEEE Trans. on Systems, Man, and Cybernetics, Vol. SMC-3, pp. 610-621, 1973. [DOI:10.1109/TSMC.1973.4309314]
31. S. Mallat, A Wavelet Tour of Signal Processing. Academic Press, San Diego, 1999. [DOI:10.1016/B978-012466606-1/50008-8]
32. G. Shaw, and D. Manolakis, "Signal processing for hyperspectral image explotation", IEEE Signal Process. Mag., Vol. 19, pp. 12, 2002. [DOI:10.1109/79.974715]
33. L. Zhuo, and J. Zheng, "A Genetic Algorithm Based Wrapper Feature Selection Method for Classification of Hyperspectral Image Using Support Vector Machine", The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 397-402, 2008. [DOI:10.1117/12.813256]
34. C. L. Huang, and C. J. Wang, "A GA-based feature selection and parameter optimization for support vector machines", Expert Systems with Application, pp. 231-240, 2006. [DOI:10.1016/j.eswa.2005.09.024]
35. D. A. Landgrebe, "Signal Theory Methods in Multispectral Remote Sensing", John Wiley & Sons, Inc., 2003. [DOI:10.1002/0471723800]
36. F. Camastra, "Signal Theory Methods in Multispectral Remote Sensing", DC Mall image and band specifications for the HYDICE Washington D.C. Mall image provided on the CD with. [Online]. Available: http://www. lars. purdue. edu/ home/ image_data/ hydice_dc_wavelengths.html.
37. N. Cristianini, and J. Shawe-Taylor, "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods", Cambridge University Press, 2000. [DOI:10.1017/CBO9780511801389]
38. F. Van der Meer, "The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery", Int. J. Appl. Earth Observation Geoinformation, Vol. 8, No. 1, pp. 3-17, 2006. [DOI:10.1016/j.jag.2005.06.001]
39. D. Akbari, A. Safari, and S. Homayouni, "Object-Based Hyperspectral Classification of Urban Areas by Using Marker-Based Hierarchical Segmentation", Photogrammetric Engineering and Remote Sensing, Vol. 80, No. 10, pp. 963-970, 2014. [DOI:10.14358/PERS.80.10.963]
Send email to the article author

Add your comments about this article
Your username or Email:


XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Aakbari D, Rokni K. Spectral-spatial classification of hyperspectral images by combining hierarchical and marker-based Minimum Spanning Forest algorithms. JGST 2023; 12 (2) :98-113
URL: http://jgst.issge.ir/article-1-1075-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 12, Issue 2 (1-2023) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology