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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摘要：在分析绿色健康植被光谱特性及5种典型地物在高分六号影像中可见光波段光谱特征的基础上，提出一种基于红、绿、蓝波段的可见光植被指数——高区分型红绿蓝植被指数（HDRGBVI），利用该指数与其他8种常见可见光植被指数提取研究区绿色植被覆盖效果进行对比研究，并采用基于SVM的监督分类方法进行对不同种可见光植被指数所提取的研究区绿色植被进行精度量化评价并将精度进行对比。结果表明：由HDRGBVI计算的植被指数灰度影像能够有效提取试验区植被，对大面积水体、狭长水体、细小水体具有抑制作用；相比其他8种常见可见光植被指数在应用于卫星影像时出现的植被与其他地类区分度低的问题，HDRGBVI增强了植被与其他地类的区分度，使得可见光植被指数应用于卫星影像时也拥有较好的提取效果；利用HDRGBVI与其他8种常见可见光植被指数分别对长桥海试验区植被覆盖区域进行提取，HDRGBVI总体精度为90.23%，Kappa系数为0.804 5，精度高于其余8种常见可见光植被指数，能够准确对长桥海试验区的植被覆盖区域进行提取；为验证HDRGBVI是否具有良好的可适用性及可靠性，分别从3景高分六号影像中选择3个研究区进行验证，分别计算三个研究区的HDRGBVI，并利用对应试验区的基于SVM的监督分类结果进行精度验证，结果表明：三个研究区的绿色植被提取精度均维持在90%左右，均可对植被覆盖区域进行有效提取提取精度受不同地类分布差异影响的波动性较小，且能够较好的削弱影像中阴影等因素的影响。综上，所提出的高区分型红绿蓝植被指数——HDRGBVI能够有效、快速、高精度、大范围提取高分六号可见光波段影像中绿色植被信息，且具有较好的适用性，为可见光植被指数与卫星影像的结合应用提供了一种可行性方法。

关键词：高分六号卫星；可见光影像；可见光植被指数；绿色植被提取

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

收稿日期: 2022-05-30 修订日期: 2022-10-07

中图分类号:

Q948

基金资助: 国家自然科学基金项目（41867040）资助

作者简介: 郑舒元，1997年生，云南农业大学水利学院硕士研究生 e-mail: 952012141@qq.com

引用本文:

郑舒元，海燕，何孟琦，王建雄. 高区分度的高分六号影像可见光波段植被指数构建[J]. 光谱学与光谱分析, 2023, 43(11): 3509-3517.
ZHENG Shu-yuan, HAI Yan, HE Meng-qi, WANG Jian-xiong. Construction of Vegetation Index in Visible Light Band of GF-6 Image With Higher Discrimination. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3509-3517.

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