Volume 14, Issue 3 (12-2017)                   JSDP 2017, 14(3): 37-50 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Khajeh Rayeni F, Ghassemian H. Spectral Unmixing Using Improved HYCA Algorithm. JSDP 2017; 14 (3) :37-50
URL: http://jsdp.rcisp.ac.ir/article-1-369-en.html
Abstract:   (5333 Views)

Hyperspectral (HS) imaging is a significant tool in remote sensing applications. HS sensors measure the reflected light from the surface of objects in hundreds or thousands of spectral bands, called HS images. Increasing the number of these bands produces huge data, which have to be transmitted to a terrestrial station for further processing. In some applications, HS images have to be sent instantly to the station requiring a high bandwidth between the sensors and the station. Most of the time, the bandwidth between the satellite and the station is narrowed limiting the amount of data that can be transmitted, and brings the idea of Compressive Sensing (CS) into the minds. In addition to the large amount of data, in these images, mixed pixels are another issue to be considered. Despite of their high spectral resolution, their spatial resolution is low causing a mixture of spectra in each pixel, but not a pure spectrum. As a result, the analysis of mixed pixels or Spectral Unmixing (SU) technique has been introduced to decompose mixed pixels into a set of endmembers and abundance fraction maps. The endmembers are extracted from spectral signatures related to different materials, and the abundance fractions are the proportions of the endmembers in each pixel. In recent years, due to the large amount of data and consequently the difficulties of real-time signal processing, and also having the ability of image compression, methods of Compressive Sensing and Unmixing (CSU) have been introduced. Two assumptions have been considered in these methods: the finite number of elements in each pixel and the low variation of abundance fractions.
HYCA algorithm is one of the methods trying to compress these kinds of data with their inherent features. One of the sensible characteristics of this algorithm is to utilize spatial information for better reconstruction of the data. In fact, HYCA algorithm splits the data cube into non-overlapping square windows and assumes that spectral vectors are similar inside each window. In this study, a real-time method is proposed, which uses the spectral information (non-neighborhood pixels) in addition to the spatial information. The proposed structure can be divided into two parts: transmitting information into the satellites and information recovery into the stations.
In the satellites, firstly, to utilize the spectral information, a new real-time clustering method is proposed, wherein the similarity between the entire pixels is not restricted to any specific form such as square window. Figure 3 shows a segmented real HS image. It can be seen that the considering square form limits the capability of the HYCA algorithm and the similarity can be found in the both neighborhood and non-neighborhood pixels. Secondly, to utilize similarity in each cluster, different measurement matrices are used. By doing this, various samples can be achieved for each cluster and further information are extracted. On the other hand, usage of different measurement matrices may affect the system stability. As a matter of fact, generating the different measurement matrices is not simple and increases complexity into the transmitters. Therefore, it conflicts with the aim of CS theory, reducing complexity into the transmitters. As a result, in the proposed method, the number of the clusters is determined by the number of the producible measurement matrices. Figure 4 shows the schematic of the proposed structure in the satellites.
In the stations, we follow HYCA procedure in equation 8 and 9, but the different similar pixels are applied to the both equations. By doing this, we reach to the improved HYCA algorithm. Finally, the proposed structure is shown in the Table 1.
To evaluate the proposed method, both real and simulated data have been used in this article. In addition, normalized mean-square error is considered as an error criteria. For the simulated data, in constant measurement sizes, the effects of the additive noise, and for real data, the effects of measurement sizes have been investigated. Besides, the proposed method has been compared with HYCA and C-HYCA and some of the traditional CS based methods. The experimental results show the superiority of the proposed method in terms of signal to noise ratios and the measurement sizes, up to  in the simulated data and  in the real data, which makes it suitable in the real-world applications.
 

Full-Text [PDF 4806 kb]   (2570 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2015/05/19 | Accepted: 2016/10/29 | Published: 2018/01/29 | ePublished: 2018/01/29

References
1. [1] F. KhajehRayeni and H. Ghassemian, "Spectral unmixing using greedy algorithm", 23th International Conference on Elelctrical Engineering (ICEE), 2015.
2. [2] H. Ghassemian, "A review of remote sensing image fusion methods," Information Fusion, vol. 32, pp. 75-89, 2016. [DOI:10.1016/j.inffus.2016.03.003]
3. [3] N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE signal processing magazine, vol. 19, pp. 44-57, 2002. [DOI:10.1109/79.974727]
4. [4] R. Rajabi and H. Ghassemian, "Spectral unmixing of hyperspectral imagery using multilayer NMF," IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 38-42, 2015. [DOI:10.1109/LGRS.2014.2325874]
5. [5] H. Ghassemian and D. Landgrebe, "Multispectral image compression by an on-board scene segmentation," in Geoscience and Remote Sensing Symposium, 2001. IGARSS'01. IEEE 2001 International, 2001, pp. 91-93. [DOI:10.1109/IGARSS.2001.976066]
6. [6] F. Kowkabi, H. Ghassemian, and A. Keshavarz, "Enhancing hyperspectral endmember extraction using clustering and oversegmentation-based preprocessing," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, pp. 2400-2413, 2016. [DOI:10.1109/JSTARS.2016.2539286]
7. [7] M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Sparse unmixing of hyperspectral data," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, pp. 2014-2039, 2011. [DOI:10.1109/TGRS.2010.2098413]
8. [8] S. K., "Introduction to Data Compression," Elsevier, 2006.
9. [9] H. Ghassemian and S. A. Hosseini, "Hyper-Spectral Data Feature Extraction Using Rational Function Curve Fitting," Signal and Data Processing, vol. 13, pp. 3-16, 2016. [DOI:10.18869/acadpub.jsdp.13.3.3]
10. [10] E. J. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on information theory, vol. 52, pp. 489-509, 2006. [DOI:10.1109/TIT.2005.862083]
11. [11] E. J. Candes and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?," IEEE transactions on information theory, vol. 52, pp. 5406-5425, 2006. [DOI:10.1109/TIT.2006.885507]
12. [12] D. L. Donoho, "Compressed sensing," IEEE Transactions on information theory, vol. 52, pp. 1289-1306, 2006. [DOI:10.1109/TIT.2006.871582]
13. [13] R. Rajabi and H. Ghassemian, "Hyperspectral data unmixing using GNMF method and sparseness constraint," in Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, 2013, pp. 1450-1453. [DOI:10.1109/IGARSS.2013.6723058]
14. [14] R. Rajabi and H. Ghassemian, "Sparsity constrained graph regularized NMF for spectral unmixing of hyperspectral data," Journal of the Indian Society of Remote Sensing, vol. 43, pp. 269-278, 2015. [DOI:10.1007/s12524-014-0408-2]
15. [15] G. Martín, J. M. Bioucas-Dias, and A. Plaza, "HYCA: A new technique for hyperspectral compressive sensing," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, pp. 2819-2831, 2015. [DOI:10.1109/TGRS.2014.2365534]
16. [16] G. Martin, J. B. Dias, and A. J. Plaza, "A new technique for hyperspectral compressive sensing using spectral unmixing," SPIE Optical Engineering Applications, vol. 8514, pp. 85140N-85140N, 2012. [DOI:10.1117/12.964374]
17. [17] C. Li, T. Sun, K. F. Kelly, and Y. Zhang, "A compressive sensing and unmixing scheme for hyperspectral data processing," IEEE Transactions on Image Processing, vol. 21, pp. 1200-1210, 2012. [DOI:10.1109/TIP.2011.2167626]
18. [18] M. Golbabaee, S. Arberet, and P. Vandergheynst, "Compressive source separation: Theory and methods for hyperspectral imaging," IEEE Transactions on Image Processing, vol. 22, pp. 5096-5110, 2013. [DOI:10.1109/TIP.2013.2281405]
19. [19] J. Liu and J. Zhang, "Spectral unmixing via compressive sensing," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, pp. 7099-7110, 2014. [DOI:10.1109/TGRS.2014.2307573]
20. [20] A. Ramirez, G. R. Arce, and B. M. Sadler, "Spectral image unmixing from optimal coded-aperture compressive measurements," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, pp. 405-415, 2015. [DOI:10.1109/TGRS.2014.2322820]
21. [21] M. V. Afonso, J. M. Bioucas-Dias, and M. A. Figueiredo, "An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems," IEEE Transactions on Image Processing, vol. 20, pp. 681-695, 2011. [DOI:10.1109/TIP.2010.2076294]
22. [22] J. M. Bioucas-Dias and J. M. Nascimento, "Hyperspectral subspace identification," IEEE Transactions on Geoscience and Remote Sensing, vol. 46, pp. 2435-2445, 2008. [DOI:10.1109/TGRS.2008.918089]
23. [23] J. M. Nascimento and J. M. Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data," IEEE transactions on Geoscience and Remote Sensing, vol. 43, pp. 898-910, 2005. https://doi.org/10.1109/TGRS.2005.844293 [DOI:10.1109/TGRS.2004.839806]
24. [24] R. N. Clark, G. A. Swayze, K. E. Livo, R. F. Kokaly, S. J. Sutley, J. B. Dalton, et al., "Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems," Journal of Geophysical Research: Planets, vol. 108, 2003.

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Signal and Data Processing