Abstract
Hyper-spectral cameras capture images at hundreds and even thousands of wavelengths. These hyper-spectral images offer orders of magnitude more intensity information than RGB images. This information can be utilized to obtain segmentation results which are superior to those that are obtained using RGB images. However, many of the wavelengths are correlated and many others are noisy. Consequently, the hyper-spectral data must be preprocessed prior to the application of any segmentation algorithm. Such preprocessing must remove the noise and inter-wavelength correlations and due to complexity constraints represent each pixel by a small number of features which capture the structure of the image. The contribution of this paper is three-fold. First, we utilize the diffusion bases dimensionality reduction algorithm (Schclar and Averbuch in Diffusion bases dimensionality reduction, pp. 151–156, [1]) to derive the features which are needed for the segmentation. Second, we describe a faster version of the diffusion bases algorithm which uses symmetric matrices. Third, we propose a simple algorithm for the segmentation of the dimensionality reduced image. Successful application of the algorithms to hyper-spectral microscopic images and remote-sensed hyper-spectral images demonstrate the effectiveness of the proposed algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schclar, A., Averbuch, A.: Diffusion bases dimensionality reduction. In: Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015)—Volume 3: NCTA, Lisbon, Portugal, 12–14 Nov 2015, pp. 151–156 (2015)
Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit. 43(7), 2367–2379 (2010)
Cassidy, R.J., Berger, J., Lee, K., Maggioni, M., Coifman, R.R.: Analysis of hyperspectral colon tissue images using vocal synthesis models. In: Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1611–1615 (2004)
Zheludeva, V., Polonena, I., Neittaanmaki-Perttuc, N., Averbuch, A., Gronroos, P.N.M., Saari, H.: Delineation of malignant skin tumors by hyperspectral imaging using diffusion maps dimensionality reduction. Biomed. Signal Process. Control 16, 48–60 (2015)
Schclar, A., Averbuch, A.: Unsupervised segmentation of hyper-spectral images via diffusion bases. In: Proceedings of the 9th International Joint Conference on Computational Intelligence, IJCCI 2017, Funchal, Madeira, Portugal, 1–3 Nov 2017, pp. 305–312 (2017)
Li, F., Ng, M.K., Plemmons, R., Prasad, S., Zhang, Q.: Hyperspectral image segmentation, deblurring, and spectral analysis for material identification. In: Proceedings SPIE 7701, Visual Information Processing XIX (2010)
Ye, J., Wittman, T., Bresson, X., Osher, S.: Segmentation for hyperspectral images with priors. In: Proceedings of 6th International Symposium on Visual Computing, Las Vegas, NV, USA, vol. 1, pp. 1–4 (2010)
Chan, T., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. Appl. Math. 66, 1632–1648 (2006)
Li, J., Bioucas-Dias, J., Plaza, A.: Supervised hyperspectral image segmentation using active learning. In: IEEE GRSS Workshop on Hyperspectral Image and Signal Processing, vol. 1, pp. 1–4 (2010)
Bioucas-Dias, J., Figueiredo, M.: Logistic regression via variable splitting and augmented Lagrangian tools. Technical Report, Instituto Superior Tecnico, TULisbon (2009)
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)
Coifman, R.R., Lafon, S.: Diffusion maps. Applied and Computational Harmonic Analysis (special issue on Diffusion Maps and Wavelets), vol. 21, pp. 5–30 (2006)
Schclar, A.: A Diffusion Framework for Dimensionality Reduction, pp. 315–325. Springer US, Boston, MA (2008)
Schclar, A., Averbuch, A., Hochman, K., Rabin, N., Zheludev, V.: A diffusion framework for detection of moving vehicles. Digit. Signal Process. 20, 111–122 (2010)
Chung, F.R.K.: Spectral graph theory. In: AMS Regional Conference Series in Mathematics, vol. 92 (1997)
Johnson, W.B., Lindenstrauss, J.: Extensions of lipshitz mapping into hilbert space. Contemp. Math. 26, 189–206 (1984)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Schclar, A., Averbuch, A. (2019). A Diffusion Approach to Unsupervised Segmentation of Hyper-Spectral Images. In: Sabourin, C., Merelo, J.J., Madani, K., Warwick, K. (eds) Computational Intelligence. IJCCI 2017. Studies in Computational Intelligence, vol 829. Springer, Cham. https://doi.org/10.1007/978-3-030-16469-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-16469-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16468-3
Online ISBN: 978-3-030-16469-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)