Fusion of Hyperspectral and Multispectral Images Based on a Centralized Non-local Sparsity Model of Abundance Maps
Fusión de imágenes hiperespectrales y multiespectrales basado en un modelo de escacez centralizado no local de mapas de abundancias
Resumen (en_US)
Objective: Hyperspectral (HS) imaging systems are commonly used in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance of the involved tasks in such applications. In the last years, fusing the information of an HS image with high spatial resolution multispectral (MS) or panchromatic (PAN) images has been widely studied to enhance the spatial resolution. Image fusion has been formulated as an inverse problem whose solution is an HS image which assumed to be sparse in an analytic or learned dictionary. This work proposes a non-local centralized sparse representation model on a set of learned dictionaries in order to regularize the conventional fusion problem.
Methodology: The dictionaries are learned from the estimated abundance data taking advantage of the depth correlation between abundance maps and the non-local self- similarity over the spatial domain. Then, conditionally on these dictionaries, the fusion problem is solved by an alternating iterative numerical algorithm.
Results: Experimental results with real data show that the proposed method outperforms the state-of-the-art methods under different quantitative assessments.
Conclusions: In this work, we propose a hyperspectral and multispectral image fusion method based on a non-local centralized sparse representation on abundance maps. This model allows us to include the non-local redundancy of abundance maps in the fusion problem using spectral unmixing and improve the performance of the sparsity-based fusion approaches.
Resumen (es_ES)
Objetivo: Sistemas de adquisición de imagen hiperespectral (HS) son comúnmente usados en un rango diverso de aplicaciones que involucran tareas de detección y clasificación. Sin embargo, la baja resolución de imágenes hiperespectrales podría limitar el rendimiento de las tareas implicadas en dichas aplicaciones. En los últimos años, fusionar la información de una imagen HS con imágenes multiespectrales (MS) o pancromáticas (PAN) de alta resolución espacial ha sido ampliamente usado para mejorar la resolución espacial. La fusión de imágenes ha sido formulada como un problema inverso cuya solución es una imagen HS, la cual se asume escasa en un diccionario analítico o aprendido. Este trabajo propone un modelo de representación escasa centralizado no local sobre un conjunto de diccionarios aprendidos para regularizar el problema de fusión convencional.
Metodología: Los diccionarios son aprendidos a partir los mapas de abundancia estimados para explotar la correlación entre mapas de abundancia y la auto-similitud no local sobre el dominio espacial. Luego, condicionalmente sobre los diccionarios aprendidos, el problema de fusión es solucionado por un algoritmo numérico iterativo y alternante.
Resultados: Los resultados experimentales usando datos reales muestra que el método propuesto supera los métodos del estado del arte bajo diferentes métricas cuantitativas. Conclusiones: En este trabajo nosotros proponemos un método de fusion de imágenes espectrales y multiespectrales basado en un representación no-local centralizada escasa en mapas de abundancias. Este modelo permite incluir la redundancia no local en el problema de fusion usando desmezclado espectral y mejorar los resultados de los métodos de fusión basados en modelo de escaces.
Descargas
Referencias
Arias, K., Vargas, E., & Arguello, H. (2019, September). Hyperspectral and Multispectral Image Fusion based on a Non-locally Centralized Sparse Model and Adaptive Spatial-Spectral Dictionaries. In 2019 27th European Signal Processing Conference (EUSIPCO) (pp. 1-5). IEEE. https://doi.org/10.23919/EUSIPCO.2019.8903001
Beck, A., & Teboulle, M. (2009). A fast-iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences, 2(1), 183-202. https://doi.org/10.1137/080716542
Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013). Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and remote sensing magazine, 1(2), 6-36. https://doi.org/10.1109/MGRS.2013.2244672
Bruckstein, A. M., Donoho, D. L., & Elad, M. (2009). From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM review, 51(1), 34-81. https://doi.org/10.1137/060657704
Chakrabarti, A., & Zickler, T. (2011, June). Statistics of real-world hyperspectral images. In CVPR 2011 (pp. 193-200). IEEE.
https://doi.org/10.1109/CVPR.2011.5995660
Chan, T., Esedoglu, S., Park, F., & Yip, A. (2005). Recent developments in total variation image restoration. Mathematical Models of Computer Vision, 17(2).
Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001). Atomic decomposition by basis pursuit. SIAM review, 43(1), 129-159. https://doi.org/10.1137/S003614450037906X
Daubechies, I., Defrise, M., & De Mol, C. (2004). An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, 57(11), 1413-1457. https://doi.org/10.1002/cpa.20042
Dian, R., Fang, L., & Li, S. (2017). Hyperspectral image super-resolution via non-local sparse tensor factorization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5344-5353). https://doi.org/10.1109/CVPR.2017.411
Dong, W., Zhang, L., Shi, G., & Wu, X. (2011). Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. on image process., 20(7), 1838-1857. https://doi.org/10.1109/TIP.2011.2108306
Dong, W., Li, X., Zhang, L., & Shi, G. (2011, June). Sparsity-based image denoising via dictionary learning and structural clustering. In CVPR 2011 (pp. 457-464). IEEE. https://doi.org/10.1109/CVPR.2011.5995478
Dong, W., Zhang, L., & Shi, G. (2011, November). Centralized sparse representation for image restoration. In 2011 IEEE ICCV (pp. 1259-1266). https://doi.org/10.1109/ICCV.2011.6126377
Dong, W., Zhang, L., Shi, G., & Li, X. (2012). Nonlocally centralized sparse representation for image restoration. IEEE transactions on Image Processing, 22(4), 1620-1630. https://doi.org/10.1109/TIP.2012.2235847
Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image processing, 15(12), 3736-3745. https://doi.org/10.1109/TIP.2006.881969
Cardon, H. D. V., Álvarez, M. A., & Gutiérrez, Á. O. (2015). Representación óptima de señales MER aplicada a la identificación de estructuras cerebrales durante la estimulación cerebral profunda. Tecnura, 19(45), 15-27. https://doi.org/10.14483/udistrital.jour.tecnura.2015.3.a01
Medina Rojas, F., Arguello Fuentes, H., & Gómez Santamaría, C. (2017). A quantitative and qualitative performance analysis of compressive spectral imagers. Tecnura, 21(52), 53-67. https://doi.org/10.14483/udistrital.jour.tecnura.2017.2.a04
Velasco, A. C., García, C. A. V., & Fuentes, H. A. (2016). Un estudio comparativo de algoritmos de detección de objetivos en imágenes hiperespectrales aplicados a cultivos agrícolas en Colombia. Revista Tecnura, 20(49), 86-100.
Fu, Y., Lam, A., Sato, I., & Sato, Y. (2017). Adaptive spatial-spectral dictionary learning for hyperspectral image restoration. International Journal of Computer Vision, 122(2), 228-245. https://doi.org/10.1007/s11263-016-0921-6
Ghasrodashti, E. K., Karami, A., Heylen, R., & Scheunders, P. (2017). Spatial resolution enhancement of hyperspectral images using spectral unmixing and bayesian sparse representation. Remote Sensing, 9(6), 541. https://doi.org/10.3390/rs9060541
Hege, E. K., O'Connell, D., Johnson, W., Basty, S., & Dereniak, E. L. (2004, January). Hyperspectral imaging for astronomy and space surveillance. In Imaging Spectrometry IX (Vol. 5159, pp. 380-391). International Society for Optics and Photonics. https://doi.org/10.1117/12.506426
Lu, G., & Fei, B. (2014). Medical hyperspectral imaging: a review. Journal of biomedical optics, 19(1), 010901. https://doi.org/10.1117/1.JBO.19.1.010901
Loncan, L., De Almeida, L. B., Bioucas-Dias, J. M., Briottet, X., Chanussot, J., Dobigeon, N., ... & Tourneret, J. Y. (2015). Hyperspectral pansharpening: A review. IEEE Geoscience and remote sensing magazine, 3(3), 27-46. https://doi.org/10.1109/MGRS.2015.2440094
Mallat, S. (1999). A wavelet tour of signal processing. Elsevier. https://doi.org/10.1016/B978-012466606-1/50008-8
Mairal, J., Bach, F., Ponce, J., Sapiro, G., & Zisserman, A. (2009, September). Non-local sparse models for image restoration. In 2009 IEEE 12th ICCV (pp. 2272-2279) https://doi.org/10.1109/ICCV.2009.5459452
Middleton, E. M., Ungar, S. G., Mandl, D. J., Ong, L., Frye, S. W., Campbell, P. E., ... & Pollack, N. H. (2013). The earth observing one (EO-1) satellite mission: Over a decade in space. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2), 243-256. https://doi.org/10.1109/JSTARS.2013.2249496
Oliveira, J. P., Bioucas-Dias, J. M., & Figueiredo, M. A. (2009). Adaptive total variation image deblurring: a majorization–minimization approach. Signal processing, 1683-1693. https://doi.org/10.1016/j.sigpro.2009.03.018
Panasyuk, S. V., Yang, S., Faller, D. V., Ngo, D., Lew, R. A., Freeman, J. E., & Rogers, A. E. (2007). Medical hyperspectral imaging to facilitate residual tumor identification during surgery. Cancer biology & therapy, 6(3), 439-446. https://doi.org/10.4161/cbt.6.3.4018
Simoes, M., Bioucas‐Dias, J., Almeida, L. B., & Chanussot, J. (2014). A convex formulation for hyperspectral image super-resolution via subspace-based regularization. IEEE Transactions on Geoscience and Remote Sensing, 53(6), 3373-3388. https://doi.org/10.1109/TGRS.2014.2375320
Schaepman, M. E., Ustin, S. L., Plaza, A. J., Painter, T. H., Verrelst, J., & Liang, S. (2009). Earth system science related imaging spectroscopy—An assessment. Remote Sensing of Environment, 113, S123-S137. https://doi.org/10.1016/j.rse.2009.03.001
Schreiber, N. F., Genzel, R., Bouché, N., Cresci, G., Davies, R., Buschkamp, P., ... & Shapley, A. E. (2009). The SINS survey: SINFONI integral field spectroscopy of z∼ 2 star-forming galaxies. The Astrophysical Journal, 706(2), 1364. https://doi.org/10.1088/0004-637X/706/2/1364
Shaw, G. A., & Burke, H. K. (2003). Spectral imaging for remote sensing. Lincoln laboratory journal, 14(1), 3-28.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Tosic, I., & Frossard, P. (2011). Dictionary learning. IEEE Signal Processing Magazine, 28(2), 27-38. https://doi.org/10.1109/MSP.2010.939537
Tropp, J. A., & Wright, S. J. (2010). Computational methods for sparse solution of linear inverse problems. Proceedings of the IEEE, 98(6), 948-958. https://doi.org/10.1109/JPROC.2010.2044010
Vargas, E., Espitia, O., Arguello, H., & Tourneret, J. Y. (2018). Spectral image fusion from compressive measurements. IEEE Transactions on Image Processing, 28(5), 2271-2282. https://doi.org/10.1109/TIP.2018.2884081
Vargas, E., Arguello, H., & Tourneret, J. Y. (2019). Spectral image fusion from compressive measurements using spectral unmixing and a sparse representation of abundance maps. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 5043-5053. https://doi.org/10.1109/TGRS.2019.2895822
Wei, Q., Bioucas-Dias, J., Dobigeon, N., & Tourneret, J. Y. (2015). Hyperspectral and multispectral image fusion based on a sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3658-3668. https://doi.org/10.1109/TGRS.2014.2381272
Wei, Q., Dobigeon, N., & Tourneret, J. Y. (2015). Bayesian fusion of multi-band images. IEEE Journal of Selected Topics in Signal Processing, 9(6), 1117-1127. https://doi.org/10.1109/JSTSP.2015.2407855
Wei, Q., Bioucas-Dias, J., Dobigeon, N., Tourneret, J. Y., Chen, M., & Godsill, S. (2016). Multiband image fusion based on spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7236-7249. https://doi.org/10.1109/TGRS.2016.2598784
Yang, J., Wright, J., Huang, T. S., & Ma, Y. (2010). Image super-resolution via sparse representation. IEEE transactions on image processing, 19(11), 2861-2873. https://doi.org/10.1109/TIP.2010.2050625
Yin, H., Li, S., & Fang, L. (2013). Simultaneous image fusion and super-resolution using sparse representation. Information Fusion, 14(3), 229-240. https://doi.org/10.1016/j.inffus.2012.01.008
Zhao, Y., Yang, J., & Chan, J. C. W. (2013). Hyperspectral imagery super-resolution by spatial–spectral joint non-local similarity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2671-2679. https://doi.org/10.1109/JSTARS.2013.2292824
Zhao, Y., Yi, C., Yang, J., & Chan, J. C. W. (2014, July). Coupled hyperspectral super-resolution and unmixing. In 2014 IEEE Geoscience and Remote Sensing Symposium (pp. 2641-2644). IEEE.
Todos los textos incluidos en la Revista Tecnura están protegidos por derechos de autor. Conforme a la ley, está prohibida su reproducción por cualquier medio, mecánico o electrónico, sin permiso escrito del Comité Editorial. Los textos completos de los artículos son de acceso abierto, es decir, se pueden leer, descargar, copiar, distribuir, imprimir, buscar o vincular. Las opiniones expresadas en los artículos publicados son las de los autores y no coinciden necesariamente con las del Comité Editorial ni las de la administración de la Facultad.
Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional
Acuerdo de creación N° 10 de 1948 del Concejo de Bogotá Acreditación Institucional de Alta Calidad - Resolución N° 23096 del 15 de diciembre del 2016
