Abstract
The present study was undertaken with the objective to check effectiveness of spectral information divergence (SID) to develop spectra from image for crop classes based on spectral similarity with field spectra. In multispectral and hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to develop crop spectra from the image itself. Hence, in this study methodology suggested to develop spectra for crops based on SID. Absorption features are unique and distinct; hence, validation of the developed spectra is carried out using absorption features by comparing it with field spectra and finding average correlation coefficient r = 0.982 and computed SID equivalent r = 0.989. Effectiveness of developed spectra for image classification was computed by probability of spectral discrimination (PSD) and resulted in higher probability for the spectra developed based on SID. Image classification was carried out using field spectra and spectra assigned by SID. Overall classification accuracy of the image classified by field spectra is 78.30% and for the image classified by spectra assigned through SID-based approach is 91.82%. Z test shows that image classification carried out using spectra developed by SID is better than classification carried out using field spectra and significantly different. Validation by absorption features, effectiveness by PSD and higher classification accuracy show possibility of new approach for spectra development based on SID spectral similarity measure.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Chang, C. I. (2000). An information theoretic-based approach to spectral variability, similarity and discriminability for hyperspectral image analysis. IEEE Transaction on Information Theory, 46(5), 1927–1932.
Chang, C. I. (2003). Hyperspectral imaging: Techniques for spectral detection and classification. New York: Kluwer Academic/Plenum Publishers.
Chang, C. I., & Plaza, A. (2006). A fast iterative algorithm for implementation of pixel purity index. IEEE Transactions on Geoscience and Remote Sensing, 3(1), 63–67.
Clark, R. N., & Roush, T. L. (1984). Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research, 89, 6329–6340.
Cover, Thomas M., & Thomas, Joy A. (2006). Elements of information theory (2nd ed.). Amsterdam: Wiley Publications.
Du, H., Chang, C. I., Ren, H., D’Amico, F. M., & Jensen, J. O. (2004). New hyperspectral discrimination measure for spectral characterization. Optical Engineering, 43(8), 1777–1786.
Dwivedi, R. S., Kandrika, Sreenivas, & Ramana, K. V. (2003). Comparison of classifiers of remote-sensing data for land-use/land-cover mapping. Current Science, 86, 328–335.
Fleiss, J. L., Cohen, J., & Everitt, B. S. (1969). Large-sample standard errors of kappa and weighted kappa. Psychology Bulletin, 72, 323–327.
Fung, T., & Ledrew, E. (1988). The determination of optimal threshold levels for change detection using various accuracy indices. Photogrammetric Engineering & Remote Sensing, 54, 1449–1454.
Goncalves, R.P., Assis, L.C. & Vieria, C.A.O. (2007). Comparison of sampling methods to classification of remotely sensed images. In IV International Symposium in Precision in Agriculture, 23–25 October, Vicosa.
Jiang, X., Tang, L., Wang, C., & Wang, C. (2004). Spectral characteristics and feature selection of hyperspectral remote sensing data. International Journal of Remote Sensing, 25(1), 51–59.
Kong, X., Shu, N., Huang, W. & Fu, J. (2010). The research on effectiveness of spectral similarity measures for hyperspectral image. In Presented in 3rd International Congress on Image and Signal Processing (CISP2010), IEEE, 978-1-4244-6516-2010.
Kruse, F.A. (2008). Comparison of ATREM, ACORN, and FLAASH atmospheric corrections using low altitude AVIRIS data of Boulder, Co. USA.” Accessed September 21, 2008. http://www.hgimaging.com/FAK_Pubs.htm.
Kullback, S. (1997). Information theory and statistics. MA: Dover Gloucester.
Okin, G. S., Roberts, D. A., Murray, B., & Okin, W. J. (2000). Practical limits on hyperspectral vegetation discrimination in arid and semiarid regions. Remote Sensing of Environment, 77(2), 212–225.
Pearlman, J. S., Barry, P. S., Segal, C. C., Shepanski, J., Beiso, D., & Carman, S. L. (2003). Hyperion, a space borne imaging spectrometer. IEEE Transactions on Geosciences and Remote Sensing, 41(6), 1160–1173.
Roberts, D. A., Adams, J. B., & Smith, M. O. (1993). Discriminating green vegetation, non-photosynthetic vegetation and soils in AVIRIS data. Remote Sensing of Environment, 44(2/3), 255–270.
T sai, F., Lin, E.-K., & Yoshino, K. (2007). Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species. International Journal of Remote Sensing, 28(5–6), 1023–1039.
Van der Meer, F. (2005). The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2005.06.001.
Weng, Q. (2011). Advances in environmental remote sensing: sensors, algorithms and applications, chapter 5 (pp. 118–120) and chapter 20 (pp.513–523). Boca Raton: CRC Press, Taylor & Francis.
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Chauhan, H.J., Mohan, B.K. Effectiveness of SID as Spectral Similarity Measure to Develop Crop Spectra from Hyperspectral Image. J Indian Soc Remote Sens 46, 1853–1862 (2018). https://doi.org/10.1007/s12524-018-0845-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-018-0845-4