Abstract
A Hyperspectral Image (HSI) is an image that is acquired by means of spatial and spectral acquisitions, over an almost continuous spectrum. Pixelwise classification is an important application in HSI due to the natural spectral diversity that the latter brings. There are many works where spatial information (e.g., contextual relations in a spatial neighborhood) is exploited performing a so-called spectral-spatial classification. In this paper, the problem of spectral-spatial classification is addressed in a different manner. First a transformation based on morphological operators is used with an example on additive morphological decomposition (AMD), resulting in a 4-way block of data. The resulting model is identified using tensor decomposition. We take advantage of the compact form of the tensor decomposition to represent the data in order to finally perform a pixelwise classification. Experimental results show that the proposed method provides better performance in comparison to other state-of-the-art methods.
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Notes
- 1.
The number of ways of an array refers to the number of its indices. A HSI is typically a three-way array of dimensions \(I_1\times I_2\times J\), where \(I_1\) and \(I_2\) are space dimensions (i.e. pixels) and J denotes the number of spectral bands.
References
Bruzzone, L., Carlin, L.: A multilevel context-based system for classification of very high spatial resolution images. IEEE Trans. Geosci. Remote Sens. 44(9), 2587–2600 (2006)
Duarte-Carvajalino, J.M., Sapiro, G., Vélez-Reyes, M., Castillo, P.E.: Multiscale representation and segmentation of hyperspectral imagery using geometric partial differential equations and algebraic multigrid methods. IEEE Trans. Geosci. Remote Sens. 46(8), 2418–2434 (2008)
Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields. IEEE Trans. Geosci. Remote Sens. 50(3), 809–823 (2012)
Dalla Mura, M., Benediktsson, J.A., Waske, B., Bruzzone, L.: Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans. Geosci. Remote Sens. 48(10), 3747–3762 (2010)
Fauvel, M., Chanussot, J., Benediktsson, J.A.: A spatial-spectral kernel-based approach for the classification of remote-sensing images. Pattern Recogn. 45(1), 381–392 (2012)
Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.: Advances in spectral-spatial classification of hyperspectral images. Proc. IEEE 101(3), 652–675 (2013)
Ghamisi, P., Dalla Mura, M., Benediktsson, J.A.: A survey on spectral-spatial classification techniques based on attribute profiles. IEEE Trans. Geosci. Remote Sens. 53(5), 2335–2353 (2015)
Ghamisi, P., et al.: Frontiers in spectral-spatial classification of hyperspectral images. IEEE Geosci. Remote Sens. Mag. (2018)
Benediktsson, J.A., Pesaresi, M., Amason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sens. 41(9), 1940–1949 (2003)
Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.: Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. Geosci. Remote Sens. 43(3), 480–491 (2005)
Dalla Mura, M., Benediktsson, J.A., Chanussot, J., Bruzzone, L.: The evolution of the morphological profile: from panchromatic to hyperspectral images. In: Prasad, S., Bruce, L., Chanussot, J. (eds.) Optical Remote Sensing, pp. 123–146. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-14212-3_8
Cavallaro, G., Dalla Mura, M., Benediktsson, J.A., Plaza, A.: Remote sensing image classification using attribute filters defined over the tree of shapes. IEEE Trans. Geosci. Remote Sens. 54(7), 3899–3911 (2016)
Velasco-Forero, S., Angulo, J.: Classification of hyperspectral images by tensor modeling and additive morphological decomposition. Pattern Recogn. 46(2), 566–577 (2013)
Comon, P.: Tensors: a brief introduction. IEEE Sig. Proc. Mag. 31(3), 44–53 (2014). hal-00923279
Huang, K., Sidiropoulos, N.D., Liavas, A.P.: A flexible and efficient algorithmic framework for constrained matrix and tensor factorization. IEEE Trans. Sign. Process. 64(19), 5052–5065 (2016)
Cohen, J., Farias, R.C., Comon, P.: Fast decomposition of large nonnegative tensors. IEEE Sign. Process. Lett. 22(7), 862–866 (2015)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm
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Jouni, M., Dalla Mura, M., Comon, P. (2019). Classification of Hyperspectral Images as Tensors Using Nonnegative CP Decomposition. In: Burgeth, B., Kleefeld, A., Naegel, B., Passat, N., Perret, B. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2019. Lecture Notes in Computer Science(), vol 11564. Springer, Cham. https://doi.org/10.1007/978-3-030-20867-7_15
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