ALL-RUSSIA OPEN ANNUAL CONFERENCES ON

**CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE**

*Principal physics, methods and techniques for monitoring the environment, potentially dangerous phenomena and objects*

Boris M. Balter^{1}, Victor V. Egorov^{1}, Vladimir A. Kottsov^{1}, Vladimir A. Kottsov^{2}

- Space Research Institute, Russian Academy of Sciences, Moscow, Russia

Balter@mail.ru - Russian State Social University, Moscow, Russia

Faminskaya@mail.ru

We describe a method for recognition of template objects in multi- and hyperspectral remote sensing data. The matrices of correlation between spectral channels are compared to correlation matrices of templates. The correlation between these matrices is a measure of similarity (double correlation, DC). Templates are recognized in data using the maximum of DC between a template and a fragment of data. The method is sensitive to spatial variations within the fragment as a complement to maximum likelihood (ML) method of classification based on averaged spectra. We add DC to ML in classification of multitemporal Landsat data stacked like a hyperspectral cube for surface types important for air pollution dispersion. For three surface targets for DC (industrial, dense residential and low intensity residential), similar spectrally but different spatially, the effect of DC measured by improvement of the sum of missed target and false alarm probabilities is 2% - 14%.

Keywords: correlation matrix, hyperspectral and multispectral data, template objects, maximum likelihood, pollutant dispersion, probability of recognition, probability of false alarm

References:

- [1] Balter B.M., Egorov V.V., Kottsov V.A., Stal’naya M.V., Korrelyatsionnye portrety giperspektral’nykh dannykh distantsionnogo zondirovaniya (Correlation portraits of hyperspectral remote sensing data), Vserossiyskaya nauchno-tekhnicheskaya konferentsiya “Sovremennye problem opredeleniya orientatsii i navigatsii kosmicheskikh apparatov (All-Russia Scientific and Technical Conference ―Current Problems of Orientation and Navigation of Spacecraft) , Moscow, 2009, No. 1, pp. 510-518 (In Russian).
- [2] Balter B.M., Egorov V.V., Kottsov V.A., Novye vozmozhnosti korrelyatsionnogo analiza dlya system tekhnicheskogo zreniya (New capabilities of correlation analysis for systems of technical vision), Tekhnicheskoe zrenie, 2017, No. 1, pp. 53-59 (In Russian).
- [3] Chekalina T.I., Popova I.V., Balter B.M., Egorov V.V., Correlation portraits and neural networks for spaceborne high-resolution spectrometry, Proceedings of ISSSR International Symposium, Maui, Hawaii, 1992, Vol. 2, pp. 1137-1149.
- [4] Lee C., Landgrebe D., Analyzing High Dimensional Multispectral Data, IEEE Transactions on Geoscience and Remote Sensing, 1993, Vol. 31, pp. 792-800, DOI: 10.1109/36.239901.
- [5] Balter B.M., Egorov V.V., Kuz’min A.A., Chekalina T.I., Primenenie spektral’no-korrelyatsionnykh metodov i teorii katastrof v izuchenii prostranstvennoi neodnorodnosti zemnoi poverkhnosti (Using spectro-correlational methods and catastrophe theory for studies of spatial nonuniformity of Earth surface), Isslevovanie Zemli iz kosmosa, 1991, Vol. 10, No. №5, pp. 10-15 (In Russian).
- [6] Popa A., Balter B.M., Ganzorig M., Egorov V.V., Osobennosti korrelyatsionnoi struktury spektra opticheskogo signala, voskhodyashchego ot zondiruemykh ob’ektov na primere morskoi poverkhnosti (Characteristics of spectral correlation structure of optical signal from remotely sensed objects exemplified by sea surface), Isslevovanie Zemli iz kosmosa, 1988, Vol. 7, No. 3, pp. 23–30 (In Russian).
- [7] Biehl L., Landgrebe D. MultiSpec—a tool for multispectral–hyperspectral image data analysis, Computers & Geosciences, December 2002, Vol. 28, No. 10, pp. 1153-1159, DOI: 0.1016/S0098-3004(02)00033-X.
- [8] Balter B.M., Egorov V.V., Kottsov V.A., Obrabotka giperspektral’nykh dannykh po Zemle i Marsu (Processing of hyperspectral data on Earth and Mars), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2006, 3(1), pp. 68-76 (In Russian), DOI: 10.21046/2070-7401-2018-15-4-12-16.

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