Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Catadioptric hyperspectral imaging, an unmixing approach

© The Institution of Engineering and Technology
Received 15/10/2019, Accepted 11/05/2020, Revised 08/04/2020, Published 12/05/2020

Hyperspectral imaging systems provide dense spectral information on the scene under investigation by collecting data from a high number of contiguous bands of the electromagnetic spectrum. The low spatial resolutions of these sensors frequently give rise to the mixing problem in remote sensing applications. Several unmixing approaches are developed in order to handle the challenging mixing problem on perspective images. On the other hand, omnidirectional imaging systems provide a 360-degree field of view in a single image at the expense of lower spatial resolution. In this study, we propose a novel imaging system which integrates hyperspectral cameras with mirrors so on to yield catadioptric omnidirectional imaging systems to benefit from the advantages of both modes. Catadioptric images, incorporating a camera with a reflecting device, introduce radial warping depending on the structure of the mirror used in the system. This warping causes a non-uniformity in the spatial resolution which further complicates the unmixing problem. In this context, a novel spatial–contextual unmixing algorithm specifically for the large field of view of the hyperspectral imaging system is developed. The proposed algorithm is evaluated on various real-world and simulated cases. The experimental results show that the proposed approach outperforms compared methods.

Inspec keywords: image resolution; remote sensing; mirrors; hyperspectral imaging; image sensors; geophysical image processing; cameras

Other keywords: spatial resolution; catadioptric omnidirectional imaging systems; hyperspectral cameras; remote sensing applications; spatial–contextual unmixing algorithm; electromagnetic spectrum; dense spectral information; catadioptric hyperspectral imaging system

Subjects: Optical, image and video signal processing; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Image sensors; Computer vision and image processing techniques; Geophysics computing; Geophysical techniques and equipment; Optical lenses and mirrors

References

    1. 1)
      • 6. Hirai, K., Osawa, N., Hori, M., et al: ‘High-dynamic-range spectral imaging system for omnidirectional scene capture’, J. Imaging, 2018, 4, (4), p. 53. Available at http://www:mdpi:com/2313-433X/4/4/53.
    2. 2)
      • 4. Aeromeccanica: ‘Fly360’. Available at http://www.aeromeccanica.it/.
    3. 3)
      • 45. Lloyd, S.: ‘Least squares quantization in PCM’, IEEE Trans. Inf. Theory, 1982, 28, (2), pp. 129–137. Available at http://ieeexplore:ieee:org/document/1056489/.
    4. 4)
      • 1. Thomas, C., Ranchin, T., Wald, L., et al: ‘Synthesis of multispectral images to high spatial resolution: a critical review of fusion methods based on remote sensing physics’, IEEE Trans. Geosci. Remote Sens., 2008, 46, (5), pp. 1301–1312.
    5. 5)
      • 23. Bioucas-Dias, J.M., Nascimento, J.M.P.: ‘Hyperspectral subspace identification’, IEEE Trans. Geosci. Remote Sens., 2008, 46, (8), pp. 2435–2445.
    6. 6)
      • 37. Zortea, M., Plaza, A.: ‘Spatial preprocessing for endmember extraction’, IEEE Trans. Geosci. Remote Sens., 2009, 47, (8), pp. 2679–2693.
    7. 7)
      • 18. Yamazawa, K., Yagi, Y., Yachida, M.: ‘Obstacle detection with omnidirectional image sensor hyperomni vision’. Proc.., 1995 IEEE Int. Conf. on Robotics and Automation, Nagoya, Japan, 1995, vol. 1, pp. 1062–1067.
    8. 8)
      • 42. Goenaga, M.A., Torres-Madronero, M.C., Velez-Reyes, M., et al: ‘Unmixing analysis of a time series of Hyperion images over the Guánica dry forest in Puerto Rico’, IEEE J. Sel. Topi. Appl. Earth Obs. Remote Sens., 2013, 6, (2), pp. 329–338.
    9. 9)
      • 31. Boardman, J.W.: ‘Automating spectral unmixing of AVIRIS data using convex geometry concepts’. Summaries of the 4th Annual JPL Air-borne Geosciences Workshop, Pasadena, 1993, pp. 11–14.
    10. 10)
      • 15. Iraqui, A., Dupuis, Y., Boutteau, R., et al: ‘Fusion of omnidirectional and PTZ cameras for face detection and tracking’. 2010 Int. Conf. on Emerging Security Technologies, Canterbury, UK., 2010, pp. 18–23. Available at http://ieeexplore:ieee:org/document/5600047/.
    11. 11)
      • 24. Heinz, D., Chang, C.I., Althouse, M.L.G.: ‘Fully constrained least-squares based linear unmixing [hyperspectral image classification]’. IEEE 1999 Int. Geoscience and Remote Sensing Symp., 1999. IGARSS'99 Proc., Hamburg, Germany, 1999, vol. 2, pp. 1401–1403.
    12. 12)
      • 14. Cinaroglu, I., Bastanlar, Y.: ‘A direct approach for object detection with catadioptric omnidirectional cameras’, Signal. Image. Video. Process., 2016, 10, (2), pp. 413–420. Available at http://link:springer:com/10:1007/s11760-015-0768-2.
    13. 13)
      • 47. Jia, S., Qian, Y.: ‘Constrained nonnegative matrix factorization for hyperspectral unmixing’, IEEE Trans. Geosci. Remote Sens., 2009, 47, (1), pp. 161–173.
    14. 14)
      • 10. Xue, Y., Zhu, K., Fu, Q., et al: ‘Catadioptric HyperSpectral light field imaging’. 2017 IEEE Int. Conf. on Computer Vision (ICCV), Venice, Italy, 2017, pp. 985–993. Available at http://ieeexplore:ieee:org/document/8237374/.
    15. 15)
      • 41. Somers, B., Zortea, M., Plaza, A., et al: ‘Automated extraction of image-based endmember bundles for improved spectral unmixing’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2012, 5, (2), pp. 396–408.
    16. 16)
      • 35. Yan, Y., Hua, W., Liu, X., et al: ‘Spatial–spectral preprocessing for spectral unmixing’, Int. J. Remote Sens., 2019, 40, (4), pp. 1357–1373. Available at https://www:tandfonline:com/doi/full/10:1080/01431161:2018:1524590.
    17. 17)
      • 30. Zhang, X., Sun, Y., Zhang, J., et al: ‘Hyperspectral unmixing via deep convolutional neural networks’, IEEE Geosci. Remote Sens. Lett., 2018, 15, (11), pp. 1755–1759. Available at https://ieeexplore:ieee:org/document/8432512/.
    18. 18)
      • 11. Bazin, J.C., Demonceaux, C., Vasseur, P., et al: ‘Motion estimation by decoupling rotation and translation in catadioptric vision’, Comput. Vis. Image Underst., 2010, 114, (2), pp. 254–273.
    19. 19)
      • 28. Bro, R., De Jong, S.: ‘A fast non-negativity-constrained least squares algorithm’, J. Chemom., 1997, 11, (5), pp. 393–401.
    20. 20)
      • 29. Bioucas-Dias, J.M., Plaza, A., Dobigeon, N., et al: ‘Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2012, 5, (2), pp. 354–379. Available at http://ieeexplore:ieee:org/document/6200362/.
    21. 21)
      • 33. Nascimento, J.M.P., Dias, J.M.B.: ‘Vertex component analysis: a fast algorithm to unmix hyperspectral data’, IEEE Trans. Geosci. Remote Sens., 2005, 43, (4), pp. 898–910.
    22. 22)
      • 2. Gluckman, J., Nayar, S.K.: ‘Catadioptric stereo using planar mirrors’, 2001. 1. Available at http://www1:cs:columbia:edu/CAVE/publications/pdfs/Gluckman_IJCV01:pdf.
    23. 23)
      • 17. Kawanishi, T., Yamazawa, K., Iwasa, H., et al: ‘Generation of high-resolution stereo panoramic images by omnidirectional imaging sensor using hexagonal pyramidal mirrors’. Proc. Fourteenth Int. Conf. on Pattern Recognition, Brisbane, Queensland, Australia, 1998, vol. 1, pp. 485–489.
    24. 24)
      • 7. Tominaga, S., Fukuda, T., Kimachi, A.: ‘A high-resolution imaging system for omnidirectional illuminant estimation’, J. Imaging Sci. Technol., 2008, 52, (4), p. 040907. Available at http://www:ingentaconnect:com/content/ist/jist/2008/00000052/00000004/art00010.
    25. 25)
      • 34. Xu, X., Tong, X., Plaza, A., et al: ‘A new spectral-spatial sub-pixel mapping model for remotely sensed hyperspectral imagery’, IEEE Trans. Geosci. Remote Sens., 2018, 56, (11), pp. 6763–6778. Available at https://ieeexplore:ieee:org/document/8410597/.
    26. 26)
      • 16. Kang, S., Roh, A., Nam, B., et al: ‘People detection method using graphics processing units for a mobile robot with an omnidirectional camera’, Opt. Eng., 2011, 50, (12), p. 127204. Available at http://opticalengineering:spiedigitallibrary:org/article:aspx?doi=10:1117/1:3660573.
    27. 27)
      • 9. Danilidis, K., Angelopoulou, E., Kumar, V.: ‘Multispectral omnidirectional optical sensor and methods therefor’. Google Patents, 2006. Available at https://patents:google:com/patent/US6982743.
    28. 28)
      • 26. Chang, C.I., Heinz, D.C.: ‘Constrained subpixel target detection for remotely sensed imagery’, IEEE Trans. Geosci. Remote Sens., 2000, 38, (3), pp. 1144–1159.
    29. 29)
      • 3. Bastanlar, Y.: ‘Structure-from-motion for systems with perspective and omnidirectional cameras’. PhD. Thesis, Middle East Technical University, 2009.
    30. 30)
      • 25. Heinz, D.C., Chang, C.-I.: ‘Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery’, IEEE Trans. Geosci. Remote Sens., 2001, 39, (3), pp. 529–545.
    31. 31)
      • 46. Baumgardner, M.F., Biehl, L.L., Landgrebe, D.A.: ‘220 band AVIRIS hyperspectral image data set: June 12, 1992 Indian pine test site 3’, 2015. Available at https://purr:purdue:edu/publications/1947/1.
    32. 32)
      • 36. Martin, G., Plaza, A.: ‘Region-based spatial preprocessing for endmember extraction and spectral unmixing’, IEEE Geosci. Remote Sens. Lett., 2011, 8, (4), pp. 745–749.
    33. 33)
      • 5. Technest Holdings: ‘REAL-time omni-directional hyperspectral imager | SBIR.gov’, 2006. Available at https://www:sbir:gov/sbirsearch/detail/174028.
    34. 34)
      • 40. Richards, J.A., Jia, X.: ‘Remote sensing digital image analysis: an introduction’ (Springer-Verlag Berlin Heidelberg, Berlin, Germany, 2006).
    35. 35)
      • 20. Sigernes, F., Ivanov, Y., Chernouss, S., et al: ‘Hyperspectral all-sky imaging of auroras’, Opt. Exp., 2012, 20, (25), p. 27650.
    36. 36)
      • 27. Settle, J.J., Drake, N.A.: ‘Linear mixing and the estimation of ground cover proportions’, Int. J. Remote Sens., 1993, 14, (6), pp. 1159–1177.
    37. 37)
      • 39. Martin, G., Plaza, A.: ‘Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2012, 5, (2), pp. 380–395.
    38. 38)
      • 13. Lourenco, M., Barreto, J.P., Vasconcelos, F.: ‘sRD-SIFT: keypoint detection and matching in images with radial distortion’, IEEE Trans. Robot., 2012, 28, (3), pp. 752–760. Available at http://ieeexplore:ieee:org/document/6151178/.
    39. 39)
      • 32. Winter, M.E.: ‘N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data’. SPIE's Int. Symp. on Optical Science, Engineering, and Instrumentation, Denver, CO, USA., 1999, pp. 266–275.
    40. 40)
      • 43. Roberts, D.A., Gardner, M., Church, R., et al: ‘Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models’, Remote Sens. Environ., 1998, 65, (3), pp. 267–279.
    41. 41)
      • 44. Degerickx, J., Roberts, D.A., Somers, B.: ‘Enhancing the performance of multiple endmember spectral mixture analysis (MESMA) for urban land cover mapping using airborne lidar data and band selection’, Remote Sens. Environ., 2019, 221, pp. 260–273. Available at https://www:sciencedirect:com/science/article/pii/S0034425718305352.
    42. 42)
      • 19. Jechow, A., Kyba, C., Hölker, F.: ‘Beyond all-sky: assessing ecological light pollution using multi-spectral full-sphere fisheye lens imaging’, J. Imaging, 2019, 5, (4), p. 46. Available at https://www:mdpi:com/2313-433X/5/4/46.
    43. 43)
      • 8. Karaca, A.C., Erturk, A., Gullu, M.K., et al: ‘Ground-based panoramic stereo hyperspectral imaging system with multiband stereo matching’, IEEE J.Sel. Topi. Appl. Earth Obs. Remote Sens., 2016, 9, (9), pp. 3926–3940. Available at http://ieeexplore:ieee:org/document/7302539/.
    44. 44)
      • 22. Onoe, Y., Yamazawa, K., Takemura, H., et al: ‘Telepresence by realtime view-dependent image generation from omnidirectional video streams’, Comput. Vis. Image Underst., 1998, 71, (2), pp. 154–165. Available at https://www:sciencedirect:com/science/article/pii/S1077314298907056.
    45. 45)
      • 12. Ozisik, N.D., Lopez-Nicolas, G., Guerrero, J.J.: ‘Scene structure recovery from a single omnidirectional image’. 2011 IEEE Int. Conf. on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain, 2011, pp. 359–366.
    46. 46)
      • 38. Plaza, A., Martinez, P., Pérez, R., et al: ‘Spatial/spectral endmember extraction by multidimensional morphological operations’, IEEE Trans. Geosci. Remote Sens., 2002, 40, (9), pp. 2025–2041.
    47. 47)
      • 21. Baker, S., Nayar, S.K.: ‘A theory of catadioptric image formation’. Sixth Int. Conf. on Computer Vision, Bombay, India, 1998, pp. 35–42.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2019.0784
Loading

Related content

content/journals/10.1049/iet-cvi.2019.0784
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address