Advanced Method for Small-Size Targets Detection in Hyperspectral Image

Main Article Content

Vitaly V. Andronov

Abstract

The advanced method for subpixel detection of small-size targets on hyperspectral image is described. The method is based on matched filtering model with the succeeding correction of determined pixel fractions. Correction consists of two stages. First one is a statistical adjustment for actual set of targets/backgrounds in a scene and second one is a pixel-wise consideration of radiometric separability of spectra. The proposed advanced method provides more exact subpixel detection of small-size targets in hyperspectral image.

Keywords:
Hyperspectral imagery, matched filtering, subpixel target detection, pixel fraction.
Published: Feb 13, 2019

Article Details

How to Cite
Andronov, V. V. (2019). Advanced Method for Small-Size Targets Detection in Hyperspectral Image. Journals of Georgian Geophysical Society, 21(2). Retrieved from https://ggs.openjournals.ge/index.php/GGS/article/view/2528
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Articles

References

Bekő L., Hunyadi G. Laakso K., Nygrén P. Identification of materials using aerial hyperspectral images // Acta Carolus Robertus, 2016.– Vol.6.– No.1.– P.19-26.

Cohen Y., August Y., Blumberg D.G., Rotman S.R. Evaluating subpixel target detection algorithms in hyperspectral imagery // Journal of Electrical and Computer Engineering, 2012.– Vol.12.– A.103286.– 15 p.

Chang C.-I. Hyperspectral Imaging: Techniques for Spectral Detection and Classification.– N.Y.: Kluwer Academic/Plenum Publishers, 2003.– 396 p.

Manolakis D., Siracusa C., Shaw G. Hyperspectral subpixel target detection using the linear mixing model // IEEE Transactions on Geoscience and Remote Sensing, 2001.– Vol.39 – No.7.– P.1392-1409.

Bateson C.A., Asner G.P., Wessman C.A. Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis // IEEE Transactions on Geoscience and Remote Sensing, 2000.– Vol.38.– No.2.– P.1083-1094.

Plaza A., Martinez P., Perez R., Plaza J. A Quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data // IEEE Transactions on Geoscience and Remote Sensing, 2004.– Vol.42.– No.3.– P.650-663.

Lukin V.V., Ponomarenko N.N., Zelensky A.A., Kurekin A.A., Lever K. Compression and classification of noisy multichannel remote sensing images // Proceedings of the SPIE, 2008.– Vol.7109.– A.71090W.– 12 p.

Bitar A.W., Cheong L.-F., Ovarlez J.-P. Target and background separation in hyperspectral imagery for automatic target detection // Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018).– Calgary: IEEE, 2018.– P.1598-1602.

Wei Y., Zhu X., Li C., Guo X., Yu X., Chang C., Sun H. Applications of hyperspectral remote sensing in ground object identification and classification // Advances in Remote Sensing, 2017.– Vol.6.– No.3.– P.201-211.

Kerekes J.P., Baum J.E. Spectral imaging system analytical model for subpixel object detection // IEEE Transactions on Geoscience and Remote Sensing, 2002.– Vol.40.– No.5.– P.1088-1101.

Melesse A.M. Remote sensing sensors and applications in environmental resources mapping and modelling / A.M. Melesse, Q. Weng, P.S. Thenkabail, G.B. Senay // Sensors, 2007.– Vol.7.– No.12.– P.3209-3241.

Nielsen A.A. Spectral mixture analysis: Linear and semi-parametric full and iterated partial unmixing in multi- and hyperspectral image data // International Journal of Computer Vision, 2001.– Vol.42.– No.1-2.– P.17-37.

Eismann M.T., Hardie R.C. Stochastic spectral unmixing with enhanced endmember class separation // Applied Optics, 2004.– Vol.43.– No.36.– P.6596-6608.

Meola J. Examining the impact of spectral uncertainty on hyperspectral data exploitation / Proceedings of the SPIE, 2018.– Vol.10644.– A.106440L.– 12 p.

Villa A., Chanussot J., Benediktsson J.A., Jutten C. Unsupervised classification and spectral unmixing for sub-pixel labelling // Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS 2011).– Vancouver: IEEE, 2011.– P.71-74.

tankevich S.A., Shklyar S.V. Land-cover classification on hyperspectral aerospace images by spectral endmembers unmixing // Journal of Automation and Information Sciences, 2006.– Vol.38.– No.12.– P.31-41.

Dadon M.M., Rotman S.R., Blumbergn D.G., Adler-Golden S., Conforti P. Target detection in the presence of multiple subpixel targets in complex backgrounds // Proceedings of the 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2016).– Los Angeles: IEEE, 2016.– P.51-54.

Kwan C., Ayhan B., Chen G., Wang J., Ji B., Chang C.-I. A Novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents // IEEE Transactions on Geoscience and Remote Sensing, 2006.– Vol.44. – No.2. – P.409-419.

Stankevich S.A., Shklyar S.V. Advanced algorithm for endmembers unmixing on hyperspectral image // Proceedings of the 1st Ukrainian Conference with International Participation “Earth Observations for Sustainable Development and Security”.– Kiev: Naukova Dumka, 2008.– P.85-89, (in Ukrainian).

Stankevich S.A., Kharytonov M.M., Kozlova A.A., Korovin V.Yu., Svidenyuk M.O., Valyaev A.M. Soil contamination mapping with hyperspectral imagery: Pre-Dnieper chemical plant (Ukraine) case study / Hyperspectral Imaging in Agriculture, Food and Environment / A.I.L. Maldonado, H. Rodriguez-Fuentes, J.A.V. Contreras (Eds).– London: IntechOpen, 2018.– P.121-136.

Fauvel M., Chanussot J., Benediktsson J.A. Decision fusion for hyperspectral classification // Hyperspectral Data Exploitation: Theory and Applications / C.-I. Chang (Ed).– N.Y.: John Wiley, 2007.– P.315-352.

Popov M.A., Stankevich S.A., Lischenko L.P., Lukin V.V., Ponomarenko N.N. Processing of hyperspectral imagery for contamination detection in urban areas // Environmental Security and Ecoterrorism / H. Alpas, S.M. Berkowicz, I.V. Ermakova (Eds).– Dordrecht: Springer, 2011.– P.147-156.

Jolad S., Roman A., Shastry M.C., Gadgil M., Basu A. A new family of bounded divergence measures and application to signal detection // Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2016).– Rome: Sapienza Università di Roma, 2016.– Vol.1.– P.72-83.

Stankevich S.A. Algorithm for statistical classification of remote sensing objects by their spectral-topological features // Scientific Bulletin of National Mining University, 2006.– No.7.– P.38-40, (in Ukrainian).

Goudail F. Bhattacharyya distance as a contrast parameter for statistical processing of noisy optical images / F. Goudail, P. Réfrégier, G. Delyon // Journal of the Optical Society of America, 2004.– Vol.21.– No.7.– P.1231-1240.

Yao S., Lin W., Ong E.P., Lu Z. Contrast signal-to-noise ratio for image quality assessment // Proceedings of International Conference on Image Processing (ICIP’05).– Genova: IEEE, 2005.– Vol.1.– P.397.

Treibitz T., Schechner Y.Y. Resolution loss without imaging blur // Journal of the Optical Society of America A, 2012.– Vol.29.– No.8.– P.1516-1528.

Manolakis D., Marden D., Shaw G.A. Hyperspectral image processing for automatic target detection applications // Lincoln Laboratory Journal, 2003.– Vol.14.– No.1.– P.79-116.