ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 4, pp. 27-35

Using the orthogonal projection method to identify small objects in multispectral analysis

A.V. Gerus 1 , E.V. Savchenko 1 , V.P. Savorskiy 1, 2 
1 V. A. Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch, Fryazino, Moscow region, Russia
2 Space Research Institute RAS, Moscow, Russia
Accepted: 19.07.2018
DOI: 10.21046/2070-7401-2018-15-4-27-35
A method for recognizing small objects (less than the resolution of equipment) on a known background in a multispectral analysis is proposed. The method is based on four main principles. First, the normalized spectra are used rather than the initial ones, which leads to a significant variability reduction for all backgrounds studied. Second, a special calibration is employed, when all signal and background spectral components are normalized to their average values for the background with the corresponding weight coefficients. Third, a procedure is applied for calculating the modules of the orthogonal projection onto the normalized background and the spectrum vectors under study, which should have a minimum value for the correct hypothesis about the unknown signal. The fourth principle is statistical processing of the obtained results when spectra of those different points are taken into account where only the background is present. The results of identification of different objects by our method were compared with a known method based on the minimization of the standard deviation of the calculated value from the investigated one. 40 spectra of different background points were selected and four objects on airfield near Moscow were considered. For the background fraction in the mixture up to 85−95 %, the simulation showed a confident recognition of all four objects under consideration, which in almost all cases was much better than the results obtained by the mean square method.
Keywords: orthogonal projection, variability, extended multidimensional space
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References:

  1. Gerus A. V., Gerus T. G., Akustoopticheskie metody identifikatsii ob″ektov v giperspektral’nom analize (Acoustooptical methods for identifying objects in hyperspectral analysis), Fizicheskie osnovy priborostroeniya, 2015, Vol. 4, No. 4, pp. 70–83.
  2. Zhuravel Yu. N., Fedoseev A. A., Osobennosti obrabotki giperspektral’nykh dannykh distantsionnogo zondirovaniya pri reshenii zadach monitoringa okruzhayushchei sredy (Features of processing of hyperspectral remote sensing data in solving environmental monitoring problems), Komp’yuternaya optika, 2013, Vol. 37, No. 4, pp. 471–476.
  3. Ignatiev V. Yu., Matveev I. A., Murynin A. B., Trekin A. N., Metod povysheniya razresheniya kosmicheskikh izobrazhenii s ispol’zovaniem apriornoi informatsii v vektornoi forme dlya sokhraneniya granits (The method of increasing the resolution of space images using a priori information in a vector form to preserve the boundaries), Vestnik Moskovskogo gosudarstvennogo tekhnicheskogo universiteta im. N. E. Baumana. Seriya “Estestvennye nauki”, 2017, pp. 1717–1730.
  4. Loupian E. A., Proshin A. A., Burtsev M. A., Balashov I. V., Bartalev S. A., Efremov V. Yu., Kashnitsky A. V., Mazurov A. A., Matveev A. M., Sudneva O. A., Sychugov I. G., Tolpin V. A., Uvarov I. A., Tsentr kollektivnogo pol’zovaniya sistemami arkhivatsii, obrabotki i analiza sputnikovykh dannykh IKI RAN dlya resheniya zadach izucheniya i monitoringa okruzhayushchei sredy (Center for the collective use of the systems of archiving, processing and analysis of satellite data of the IKI RAN for solving problems of studying and monitoring the environment), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 5, pp. 263−284.
  5. Hafizov D. G., Sintez i analiz algoritmov raspoznavaniya izobrazhenii prostranstvennykh gruppovykh tochechnykh ob″ektov: Diss. kand. tekhn. nauk (Synthesis and analysis of image recognition algorithms for spatial group point objects: Cand. techn. sci. thesis), Yoshkar-Ola, 2004, 151 p.
  6. Chetvertakov A. N., Obnaruzhenie ob″ektov minimal’nogo kontrasta na tsifrovykh izobrazheniyakh (Detecting objects of minimal contrast on digital images), Gaudeamus, 2013, No. 2(22), pp. 92–95.
  7. Amro I., Mateos J., Vega M., Molina R., Katsaggelos A. K., A survey of classical methods and new trends in pansharpening of multispectral images, EURASIP J. Advances in Signal Processing, 2011, pp. 2–19.
  8. Loncan L., Almeida L. B., Bioucas-Dias J. M., Briottet X., Chanussot J., Dobigeon N., Fabre S., Liao W., Licciardi G. A., Simões M., Tourneret J.-I., Veganzones M. A., Vivone G., Wei Q., Yokoya N., Hyperspectral pansharpening, IEEE Geoscience and Remote Sensing Magazine, 2015, No. 3, pp. 27–46.
  9. Manolakis D., Shaw G., Detection Algorithms for Hyperspectral Imaging Applications, IEEE Signal Processing Magazine, 2002, Vol. 19, No. 1, pp. 378–384.
  10. Manolakis D., Marden D., Shaw G., Hyperspectral Image Processing for Automatic Target Detection Applications, Lincoln Laboratory J., 2003, Vol. 14, No. 1, pp. 79–115.
  11. Pearlman J., Carman S., Segal C., Jarecke P., Barry P., Overview of the Hyperion Imaging Spectrometer for the NASA EO-1 Mission, Intern. Geoscience and Remote Sensing Symp. (IGARSS’01), Proc. Conf. IEEE, 2001, Vol. 7, pp. 3036−3038.