高区分度的高分六号影像可见光波段植被指数构建

doi:10.3964/j.issn.1000-0593(2023)11-3509-09

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Abstract Based on the analysis of the spectral characteristics of healthy green vegetation and the spectral characteristics of five typical ground objects in the visible light band of GF-6 image, a visible light vegetation index based on the red, green and blue bands, high discrimination red green blue vegetation index (HDRGBVI), is proposed, This index is used to compare the effect of extracting green vegetation cover in the study area with other eight common visible vegetation indexes, and the SVM based supervised classification method is used to quantitatively evaluate the accuracy of green vegetation extracted by different visible vegetation indexes in the study area and compare the accuracy. The results show that the vegetation index gray-scale image calculated by HDRGBVI can effectively extract the vegetation in the experimental area, and has an inhibitory effect on large-area water bodies, long and narrow water bodies, and small water bodies; Compared with the low differentiation between vegetation and other land types when other eight common visible vegetation indexes are applied to satellite images, HDRGBVI enhances the differentiation between vegetation and other land types, so that the visible vegetation index also has a better extraction effect when applied to satellite images; HDRGBVI and other eight common visible light vegetation indexes are used to extract the vegetation-coverage area of the Changqiao-Lake test area respectively. The overall accuracy of HDRGBVI is 90.23%, and the kappa coefficient is 0.804 5, which is higher than the other eight common visible light vegetation indexes, and can accurately extract the vegetation coverage area of the Changqiao-Lake test area; In order to verify whether HDRGBVI has good applicability and reliability, three study areas are selected from three high score six images for verification, and the HDRGBVI of the three study areas are calculated respectively, and the accuracy is verified by using the SVM based supervised classification results of the corresponding test areas. The results show that the extraction accuracy of green vegetation in the three study areas is maintained at about 90%, It can effectively extract the vegetation covered area, and the fluctuation of extraction accuracy affected by the distribution difference of different land types is small. It can better weaken the influence of shadow and other factors in the image. To sum up, the high classification red, green blue vegetation index HDRGBVI proposed in this paper can effectively, quickly, accurately and widely extract the green vegetation information in the GF-6 visible band image, and has good applicability, which provides a feasible method for the combination of visible vegetation index and satellite image.

Key words：GF-6 remote sensing satellite; Satellite visible light image; Visible light vegetation index; Green vegetation extraction

Received: 2022-05-30 Accepted: 2022-10-07

ZTFLH:

Q948

Cite this article:

ZHENG Shu-yuan,HAI Yan,HE Meng-qi, et al. Construction of Vegetation Index in Visible Light Band of GF-6 Image With Higher Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3509-3517.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)11-3509-09 OR https://www.gpxygpfx.com/EN/Y2023/V43/I11/3509

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