Automatized recognition of urban vegetation and water bodies by Jilin-1А satellite images
|1Makarov, AL, 2Belousov, KG, 3Svinarenko, DN, 1Khoroshylov, VS, 3Mozgovoy, DK, 1Popel', VM |
1Yangel Yuzhnoye State Design Office, Dnipro, Ukraine
2Yuzhnoye State Design Office, Dnipro, Ukraine
3Oles Honchar Dnipro National University, Dnipro, Ukraine
|Space Sci. & Technol. 2021, 27 ;(4):42-53|
|Publication Language: Ukrainian|
The results of testing the developed techniques for automatized recognition of vegetation and water bodies on the urban territory by multispectral images from the Jilin-1А satellite are given.The research included automatized recognition of vegetation and water bodies on the selected observation territory based on images with super high spatial resolution in the visual and infrared range and consequent comparison of the obtained results with the results of visual decoding.
The obtained results of processing the images from the Jilin-1А satellite in accordance with the proposed techniques confirmed the sufficiently high accuracy of automatized edge enhancement of recognized objects as compared to the results of interactive visual recognition of these images. Different test areas provided a good separation of vegetation and water types with the same thresholding customization.
The accuracy of automatized classification of vegetation and water bodies (without considering the standard errors) for different test areas was within 81...92%, and values of kappa-coefficient were within 0.68 to 0.85.
Comparison of normalized index images received from Jilin-1А and Sentinel-2A satellites showed slight discordance in NDVI values and significant discordances for NDWI and MNDWI that are caused by the usage of different spectral channels (SWIR and NIR). These discordances can be sufficiently reduced when using correction coefficients.Analysis of the influence of output image resolution reduction (from 10 to 8 bit) and subsequent informational compressing (JPEG lossy and JPEG2000 lossless) on results of automatized recognition of vegetation and water bodies confirmed the validity and efficiency of these techniques. The volume of saved and transmitted files significantly decreased (in 80…100 times) with a slight reduction of classification accuracy (by 1...2 %).
The proposed techniques make it possible to increase significantly the efficiency and probability of renewing maps of big cities and to reduce financial expenditures as compared to the traditional ground GPS-surveying and aerosurveying.
The high-level automatization of image processing and minimization of necessary calculations (as compared to techniques that use complex classifiers and neural networks) allow to implement the developed technique as a geographic information web service that satisfies the needs of a wide circle of government services and commercial structures and can be useful for megalopolis population and tourists.
|Keywords: image processing, map renewal, multispectral images, satellite monitoring, spectral indices|
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