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APPLICATION OF MULTISPECTRAL IMAGES TO SEARCH FOR CONSTRUCTION OBJECTS ON THE SPECTRAL SIGNATURES BASE | Nazyrova | Scientific Journal of Astana IT University

APPLICATION OF MULTISPECTRAL IMAGES TO SEARCH FOR CONSTRUCTION OBJECTS ON THE SPECTRAL SIGNATURES BASE

D. Nazyrova, Z. Aitkozha

Аннотация


The work is devoted to the study of Landsat-8 multispectral images of not high resolution using the spectral angle method on the base of spectral signatures libraries to detect objects under construction in an urban area. The physical basis of the research method is that all objects have different reflection coefficients depending on the wavelength. This property makes it possible to identify various substances by their spectral signatures. In the work, an automatic comparison of the curves of the spectral reflectivity of objects on a lowresolution space multispectral image was made to identify the identity of the characteristic energy absorption and reflection zones for detecting objects in the construction process. The article also describes the stages of image preprocessing, cross-track illumination correction of the image, atmospheric correction, and mathematical operations of bands transformation, which provide more opportunities for analysis and recognition of objects using a spectral study of a space image. The study accurately determines the presence or absence of the desired materials, since the search is based on the molecular structure of the substance. Also, the use of multispectral images allows you to analyze the entire city at the same time. The initial data was taken from a 2021 Landsat-8 satellite image with 11 bands, with a resolution of 30 meters, which was enhanced to 15 meters during pre-processing. The results of the search and detection of objects under construction in the city are given. The detection results can be used as input data for further in-depth analysis.

Ключевые слова


spectral signature, spectral curve, spectral library, remote sensing, multispectral image, construction objects recognition

Полный текст:

PDF (English)

Литература


Shaw, G.A., & Burke, H.K. (2003). Spectral imaging for remote sensing. Lincoln laboratory journal,

(1), 3-28.

Cherepanov, A. (2009b, August 27). Spectral libraries - sources of data on spectra. https://gis-lab.

info/qa/spectrum-lib.html

Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., & Goetz,

A.F.H. (1993). The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote sensing of environment, 44(2-3), 145-163. https://doi.

org/10.1016/0034-4257(93)90013-N

Pan, Z., Hu, Y., & Wang, G. (2019). Detection of short-term urban land use changes by combining

SAR time series images and spectral angle mapping. Frontiers of Earth Science, 13(3), 495-509.

https://doi.org/10.1007/s11707-018-0744-6

Brand, S. (2011). Roof surface classification with hyperspectral and laserscanning data: an assessment

of spectral angle mapper and support vector machines. na.

Young, N.E., Anderson, R.S., Chignell, S.M., Vorster, A.G., Lawrence, R., & Evangelista, P.H. (2017). A

survival guide to Landsat preprocessing. Ecology, 98(4), 920-932. https://doi.org/10.1002/ecy.1730

Zhuang, L., & Ng, M.K. (2020, June). Cross-track Illumination Correction For Hyperspectral Pushbroom

Sensors Using Total Variation and Sparsity Regularization. In 2020 IEEE 11th Sensor Array and MultiBand Signal Processing Workshop (SAM) (pp. 1-5). IEEE. doi: 10.1109/SAM48682.2020.9104285

Vibhute, A.D., Kale, K.V., Dhumal, R.K., & Mehrotra, S.C. (2015, December). Hyperspectral imaging

data atmospheric correction challenges and solutions using QUAC and FLAASH algorithms. In 2015

International Conference on Man and Machine Interfacing (MAMI) (pp. 1-6). IEEE. doi: 10.1007/978-

-10-3874-7_55

Rahaman KR, Hassan QK, Ahmed MR. Pan-Sharpening of Landsat-8 Images and Its Application

in Calculating Vegetation Greenness and Canopy Water Contents. ISPRS International Journal of

Geo-Information. 2017; 6(6):168. https://doi.org/10.3390/ijgi6060168

Young, N. E., Anderson, R. S., Chignell, S. M., Vorster, A. G., Lawrence, R., & Evangelista, P. H. (2017).

A survival guide to Landsat preprocessing. Ecology, 98(4), 920-932. doi: 10.1002/ecy.1730

Workman Jr, J., & Springsteen, A. (1998). Applied spectroscopy: a compact reference for practitioners.

Academic Press.

Garcia-Allende, P.B., Conde, O.M., Mirapeix, J., Cubillas, A.M., & Lopez-Higuera, J.M. (2008). Data

processing method applying principal component analysis and spectral angle mapper for imaging

spectroscopic sensors. IEEE Sensors Journal, 8(7), 1310-1316. doi: 10.1109/JSEN.2008.926923

Tembhurne, O.W., & Malik, L.G. (2012, February). Hybrid classification using combination of optimized spectral angle mapping algorithm and interpolation method on multispectral and hyper

spectral image. In 2012 International Conference on Computing, Communication and Applications (pp.

-4). IEEE. doi: 10.1109/ICCCA.2012.6179210

Cho, M.A., Debba, P., Mathieu, R., Naidoo, L., Van Aardt, J.A.N., & Asner, G.P. (2010). Improving discrimination of savanna tree species through a multiple-endmember spectral angle mapper approach: Canopy-level analysis. IEEE Transactions on Geoscience and Remote Sensing, 48(11), 4133-

doi: 10.1109/TGRS.2010.2058579

Liu, X., & Yang, C. (2013, December). A kernel spectral angle mapper algorithm for remote sensing

image classification. In 2013 6th International Congress on Image and Signal Processing (CISP) (Vol. 2,

pp. 814-818). IEEE. doi: 10.1109/CISP.2013.6745277

Ye, C.M., Cui, P., Pirasteh, S., Li, J., & Li, Y. (2017). Experimental approach for identifying building

surface materials based on hyperspectral remote sensing imagery. Journal of Zhejiang University-SCIENCE A, 18(12), 984-990. https://doi.org/10.1631/jzus.a1700149

Houska, T. (2012). EarthExplorer (No. 136). US Geological Survey




DOI: http://dx.doi.org/10.37943/EOQD2512

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(P): 2707-9031
(E): 2707-904X

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