Abstract
Hyperspectral band selection algorithms can save the computational costs during image restoration and analysis. This paper proposes a novel unsupervised band selection method, based on dynamic local cluster ratio (DLCR). The contributions of this paper can be summarized as follows. First, the similarity matrix is calculated in a novel way. Conventional approaches compute the matrix from the Euclidean distances between bands and are vulnerable to noise. Our proposed method can improve the robustness to such noise. Second, we propose an enhanced clustering strategy which clusters each band individually. Third, a dynamic ranking strategy is used to select bands iteratively. Bands that are highly correlated with each other will be prevented from being added to avoid redundancy. DLCR demonstrates improved performance on the Indian Pines and Pavia University data sets, when compared against other methods from the literature.
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References
Chang CI (2007) Hyperspectral data exploitation: theory and applications. Wiley, Hoboken, p 2007
Chang CI, Wang S (2006) Constrained band selection for hyperspectral imagery. IEEE Trans Geosci Remote Sens 44(6):1575–1585
Chang CI, Du Q, Sun TL et al (1999) A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification. IEEE Trans Geosci Remote Sens 37(6):2631–2641
De Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evolut Comput 6(3):239–251
Du Q, Yang H (2008) Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geosci Remote Sens Lett 5(4):564–568
Du Q, Fowler JE, Zhu W (2009) On the impact of atmospheric correction on lossy compression of multispectral and hyperspectral imagery. IEEE Trans Geosci Remote Sens 47(1):130–132
Feng L, Tan AH, Lim MH et al (2016) Band selection for hyperspectral images using probabilistic memetic algorithm. Soft Comput 20(12):4685–4693
Guo B, Gunn SR, Damper RI et al (2006) Band selection for hyperspectral image classification using mutual information. IEEE Geosci Remote Sens Lett 3(4):522–526
Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recognit Lett 31(8):651–666
Jia S, Ji Z, Qian Y et al (2012) Unsupervised band selection for hyperspectral imagery classification without manual band removal. IEEE J Sel Top Appl Earth Obs Remote Sens 5(2):531–543
Jia S, Tang G, Zhu J et al (2016a) A novel ranking-based clustering approach for hyperspectral band selection. IEEE Trans Geosci Remote Sens 54(1):88–102
Jia S, Xie Y, Tang G et al (2016b) Spatial-spectral-combined sparse representation-based classification for hyperspectral imagery. Soft Comput 20(12):4659–4668
Jimenez LO, Landgrebe DA (1998) Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data. IEEE Trans Syst Man Cybern Part C (Appl Rev) 28(1):39–54
Li W, Prasad S, Fowler JE et al (2012) Locality-preserving dimensionality reduction and classification for hyperspectral image analysis. IEEE Trans Geosci Remote Sens 50(4):1185–1198
Liao W, Pizurica A, Scheunders P et al (2013) Semisupervised local discriminant analysis for feature extraction in hyperspectral images. IEEE Trans Geosci Remote Sens 51(1):184–198
Pal M, Foody GM (2010) Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Remote Sens 48(5):2297–2307
Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496
Wang Q, Lin J, Yuan Y (2016) Salient band selection for hyperspectral image classification via manifold ranking. IEEE Trans Neural Netw Learn Syst 27(6):1279–1289
Wang L, Zhang J, Liu P et al (2017) Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Comput 21(1):213–221
Yuan Y, Zheng X, Lu X (2017) Discovering diverse subset for unsupervised hyperspectral band selection. IEEE Trans Image Process 26(1):51–64
Zhao YQ, Zhang L, Kong SG (2011) Band-subset-based clustering and fusion for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 49(2):747–756
Acknowledgements
We would like to express our sincere appreciation to the editors and the anonymous reviewers for their insightful comments, which have greatly helped us in improving the quality of the paper. This work was partially supported by the National Natural Science Foundation of China, under Grants 61773304 and 61371201, the Program for Cheung Kong Scholars and Innovative Research Team in University under Grant IRT_15R53. Rustam Stolkin was supported by a Royal Society Industry Fellowship.
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Shang, R., Lan, Y., Jiao, L. et al. A dynamic local cluster ratio-based band selection algorithm for hyperspectral images. Soft Comput 23, 8281–8289 (2019). https://doi.org/10.1007/s00500-018-3464-7
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DOI: https://doi.org/10.1007/s00500-018-3464-7