Dominant Sets Based Pre-Feature Selection Method for Hyperspectral Data

Year 2022, Volume: 19 Issue: 1, 51 - 61, 01.05.2022

Onur Haliloğlu , Orhan Gazi

Abstract

Hyperspectral Data has a large volume compared to panchromatic and RGB data. This
large volume can lead to processing, storage, and transmission problems. Therefore, it is
crucial to decrease the size of the hyperspectral data for practical applications. Feature
selection can be used in order to get rid of large data size problems. In this paper, a preband selection framework is presented to reduce the data size and to reduce the
complexity of a well-known band selection method in hyperspectral imagery: Sequential
Forward Selection (SFS). The proposed pre-band selection method is based on
“dominant sets”. Clustering performance of each spectral band is evaluated, and a
reduced set of spectral bands is formed based on the clustering performances. SFS is
applied to this reduced hyperspectral data. The aim of the study is to reduce the
computational complexity of SFS by applying a dominant set based pre band selection
method. Besides reducing the computational complexity of SFS method, results on Pavia
and Indian Pines datasets show that the proposed pre-feature selection method performs
slightly better than the state-of-the-art feature selection methods in terms of classification
accuracy.

Keywords

Hyperspectral image processing, Dominant sets, Band selection, Dimension reduction, Feature selection.

References

• U. Sakarya, M. Teke, C. Demirkesen, O. Haliloğlu, O. Kozal, S. Deveci, F. Öztoprak, B. U. Töreyin, and S. Z. Gürbüz, “A Short Survey of Hyperspectral Remote Sensing and Hyperspectral Remote Sensing Research at TUBİTAK UZAY” presented at RAST 2015 8th International Conference in Space Technologies, Istanbul, Turkey, 2015.
• B. Tu, Q. Ren, C. Zhou, S. Chen, and W. He, “Feature Extraction Using Multidimensional Spectral Regression Whitening for Hyperspectral Image Classification.,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 8326-8340, 2021.
• Q. Li, B. Zheng, B. Tu, J. Wang, and C. Zhou, “Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 5134- 5148, 2020.
• C. Zhang, M. Ye, L. Lei, and Y. Qian, “Feature Selection for Cross-Scene Hyperspectral Image Classification Using Cross-Domain I-ReliefF,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 5932- 5949, 2021.
• B. Feng and J. Wang, “Hyperspectral Image Dimension Reduction Using Weight Modified Tensor-Patch-Based Methods," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3367- 3380, 2020.
• L. Zhang, Q. Zhang, B. Du, X. Huang, Y. Yan Tang, and D. Tao, “Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images,” IEEE Transactions on Cybernetics, vol. 48, no. 1, pp. 16-28, 2018.
• W. Sun, Q. Du, “Hyperspectral Band Selection: A Review”, IEEE Geoscience and Remote Sensing Magazine, vol. 7, Iss. 02, pp. 118-139, 2019.
• H. Yang, Q. Du, H. Su, X. Zhao, H. Wan and J. Sun, “An Efficient Method for Supervised Hyperspectral Band Selection”, Geoscience and Remote Sensing Letters, IEEE, vol. 8, no. 1, pp. 138-142, 2011.
• R. Yang, L. Su and S. Jia, “Representative band selection for hyperspectral image classification”, Journal of Visual Communication and Image Representation, vol. 48, pp. 396-403, 2017.
• C.-I. Chang and S. Wang, “Constrained band selection for hyperspectral imagery”, Geoscience and Remote Sensing, IEEE Transactions on, vol. 44, no. 6, pp. 1575-1585, 2006.
• Q. Du and H. Yang, “Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis”, Geoscience and Remote Sensing Letters, IEEE, vol. 5, no. 4, pp. 564-568, 2008.
• A. Datta, S. Ghosh and A. Ghosh, “Clustering based band selection for hyperspectral images”, Communications, Devices and Intelligent Systems (CODIS), 2012.
• J. Kittler, “A new search algorithm for feature selection in hyperspectral remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 39, iss. 8, no. 1, pp. 1360-1367, 2001.
• J. Kittler, “Feature selection and extraction,” Handbook of pattern recognition and image processing, pp. 59-83, 1986.
• K. Riesen and H. Bunke, “IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning,” Structural, Syntactic, and Statistical Pattern Recognition, vol. 5342, pp. 287-297, 2008
• J. Shi and J. Malik, “Normalized cuts and image segmentation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888-905, 2000.
• M. Pavan and M. Pelillo, “Dominant Sets and Pairwise Clustering”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, no. 1, pp. 167-172, 2007.
• J. Hou, E. Xu, W. Liu, Q. Xia and N. Qi, “A density-based enhancement to dominant sets clustering”, IET Computer Vision, vol. 7, iss. 5, pp. 354-361, 2013.
• V. S. Anitha and M. P. Sebastian, “Dominating set clustering protocols for mobile ad hoc networks”, Dynamic AdHoc Networks, 2013.
• O. Haliloğlu, U. Sakarya and B. U. Töreyin, “Evaluation of clustering performance of hyperspectral bands,” IEEE 2015 23rd Signal Processing and Communications Applications Conference (SIU), 2015.
• M. Teke, H. Deveci, O. Haliloglu, S. Gurbuz and U. Sakarya, “A short survey of hyperspectral remote sensing applications in agriculture”, Recent Advances in Space Technologies (RAST), 2013 6th International Conference on, 2013.
• U. Sakarya, Z. Telatar and A. Alatan, “Dominant sets-based movie scene detection”, Signal Processing, vol. 92, no. 1, pp. 107-119, 2012.
• “Hyperspectral Remote Sensing Scenes”, 2014. [online]. Available: http://www.ehu.es/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.
• G. M. Foody, “Status of land cover classification accuracy assessment”, Remote Sensing of Environment, vol. 80, no. 1, pp. 185-201, 2002