Abstract
Dictionary learning and sparse coding have been widely studied as mechanisms for unsupervised feature learning. Unsupervised learning could bring enormous benefit to the processing of hyperspectral images and to other remote sensing data analysis because labelled data are often scarce in this field. We propose a method for clustering the pixels of hyperspectral images using sparse coefficients computed from a representative dictionary as features. We show empirically that the proposed method works more effectively than clustering on the original pixels. We also demonstrate that our approach, in certain circumstances, outperforms the clustering results of features extracted using principal component analysis and non-negative matrix factorisation. Furthermore, our method is suitable for applications in repetitively clustering an ever-growing amount of high-dimensional data, which is the case when working with hyperspectral satellite imagery.
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Availability of data and materials
The data sets used in our experiments are available as .mat files on the Grupo de Inteligencia Computacional website, here: http://tiny.cc/f6gnez.
Code availability
All of the code used in this project is available on Github (https://github.com/JoshuaDBruton/SparseCoefficientClustering). The repository is licensed under the GNU General Public License.
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JB received the postgraduate merit award from the University of the Witwatersrand, Johannesburg, Gauteng, which allowed him to proceed with this research project.
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JB is a student at the University of the Witwatersrand, he authored the project under the supervision of Dr. HW.
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Bruton, J., Wang, H. Dictionary learning for clustering on hyperspectral images. SIViP 15, 255–261 (2021). https://doi.org/10.1007/s11760-020-01750-z
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DOI: https://doi.org/10.1007/s11760-020-01750-z