地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (6): 1092-1105.doi: 10.12082/dqxxkx.2021.200517
赵栋梁1(), 郭超凡2, 吴东丽3, 高星琪1, 郭逍宇1,*(
)
收稿日期:
2020-05-17
修回日期:
2020-12-09
出版日期:
2021-06-25
发布日期:
2021-08-25
通讯作者:
郭逍宇
作者简介:
赵栋梁(1996— ),男,山西吕梁人,硕士生,主要事从湿地生态过程研究。E-mail: 920240936@qq.com
基金资助:
ZHAO Dongliang1(), GUO Chaofan2, WU Dongli3, GAO Xingqi1, GUO Xiaoyu1,*(
)
Received:
2020-05-17
Revised:
2020-12-09
Online:
2021-06-25
Published:
2021-08-25
Contact:
GUO Xiaoyu
Supported by:
摘要:
依托中分辨率成像光谱仪完整的数据序列和丰富的光谱信息,遥感特征指数在湿地生态系统发展变化的状态、趋向和规律研究方面发挥着不可替代的优势。传统类间距离判别的遥感特征指数选取中常存在过分依赖数据统计特征、入选指数与目标地类间生态学意义不明确、分类模型普适性差等局限性。基于此,本研究以河北省白洋淀湿地自然保护区为例,提出类可分离性距离判别(Class Separation Discrimination,CSD)与类间距离判别(Class Distance Discrimination,CDD)相结合的方法构建最优遥感特征指数集,并采用QUEST算法和马氏距离判别法构建分类决策树模型用于白洋淀湿地信息的提取研究,尝试克服传统类间距离指数选取中的不足。结果表明:运用CSD和CDD相结合的方法所选取的遥感特征指数在研究区湿地信息提取过程中的总体分类精度达到了91.32%,Kappa系数0.88,较传统的分类与回归树(Classification and Regression Tree,CART)方法,分类精度提高了1.67%;其次选取的最优指数与待提取的湿地类型均具有明确的生态学意义,如挺水植物在立地干湿交替条件下的潴育化过程决定了氧化铁比率IO可成功的将混分的耕地和挺水植物进一步分离;进一步将基于研究区2017年OLI影像构建的CSD和CDD相结合方法与CART方法的模型分别应用于研究区2019年OLI影像进行分类,基于CSD和CDD相结合方法构建的模型分类总体精度和Kappa系数分别为:86.97%、0.83,基于CART方法构建的模型无法满足分类需求,研究结果较好地证明了基于CSD和CDD相结合方法构建的模型在年际之间具有良好的适用性和稳定性。总之,CSD和CDD相结合的方法在不降低湿地信息提取精度的基础上,有效避免了传统遥感特征指数选择方法的局限性,提高了分类模型的普适性,是遥感特征指数选择算法和决策树相结合在湿地信息提取方面的有益尝试。
赵栋梁, 郭超凡, 吴东丽, 高星琪, 郭逍宇. CSD和CDD结合下的最优遥感特征指数集构建及其在湿地信息提取中的应用[J]. 地球信息科学学报, 2021, 23(6): 1092-1105.DOI:10.12082/dqxxkx.2021.200517
ZHAO Dongliang, GUO Chaofan, WU Dongli, GAO Xingqi, GUO Xiaoyu. Construction of Optimal Remote Sensing Feature Index Set based on CSD and CDD and Its Application in Wetland Information Extraction[J]. Journal of Geo-information Science, 2021, 23(6): 1092-1105.DOI:10.12082/dqxxkx.2021.200517
表3
遥感特征指数集"
特征类 | 遥感特征指数 | 简称 | 计算方法 | 参考文献 |
---|---|---|---|---|
绿度指数 | 差值植被指数 | DVI | NIR-Red | [ |
归一化差值植被指数 | NDVI | (NIR-Red)/(NIR+Red) | [ | |
重归一化植被指数 | RDVI | (NIR-Red)/(SQRT(NIR+Red)) | [ | |
绿色归一化植被指数 | GNDVI | (NIR-Green)/(NIR+Green) | [ | |
比值植被指数 | RVI | NIR/Red | [ | |
缨帽变换绿度分量 | GVI | (-0.2848)×Blue+(-0.2435)×Green+(-0.5436)×Red+(0.7243)×NIR+(0.0840)×SWIR_1+(-0.1800)×SWIR_2 | [ | |
黄度指数 | 归一化差值耕作指数 | NDTI | (SWIR_1-SWIR_2)/(SWIR_1+SWIR_2) | [ |
归一化衰败植被指数 | NDSVI | (SWIR_1-Red)/(SWIR_1+Red) | [ | |
消除大气影响的植被指数 | 转换差值植被指数 | TDVI | 1.5×(NIR-Red)/SQRT(NIR 2+Red 2+0.5) | [ |
大气阻抗植被指数 | ARVI | (NIR-(2×Red-Blue))/(NIR+(2×Red-Blue)) | [ | |
增强型植被指数 | EVI | 2.5×((NIR-Red)/(NIR+6×Red-7.5×Blue+1)) | [ | |
可见光大气阻抗指数 | VARI | (Green-Red)/(Green+Red-Blue) | [ | |
消除土壤背景影像的植被 指数 | 修改型非线性植被指数 | MNLI | ((NIR2-Red) (1+0.5))/(NIR2+Red+0.5) | [ |
垂直植被指数 | PVI | (NIR-0.96916×Red-0.084726)/SQRT (1+0.969162) | [ | |
土壤调节植被指数 | SAVI | 1.5×(NIR-Red)/(NIR+Red+0.5) | [ | |
修改型土壤调节植被 指数 | MSAVI | 0.5×(2×NIR+1-SQRT((2×NIR+1)2)-8×(NIR-Red))) | [ | |
优化土壤调节植被指数 | OSAVI | (NIR-Red)/(NIR+Red+0.16) | [ | |
湿度指数 | 缨帽变换湿度分量 | WVI | (0.1509)×Blue+0.1973×Green+0.3279×Red+0.3406×NIR-0.7112×SWIR_1-0.4572×SWIR_2 | [ |
归一化红外指数 | NDII | (NIR-SWIR_1)/(NIR+SWIR_1) | [ | |
归一化差异水体指数 | NDWI | (Green-NIR)/(Green+NIR) | [ | |
改进的归一化差异水体指数 | MNDWI | (Green-SWIR_1)/(Green-SWIR_1) | [ | |
建筑指数 | 归一化建筑指数 | NDBI | (SWIR_1-NIR)/(SWIR_1-NIR) | [ |
改进的归一化裸露指数 | MNDBI | NDBI+(1-NDVI) | [ | |
归一化三波段指数 | NDTBI | (SWIR_2+SWIR_1-Red)/(SWIR_2+SWIR_1+Red) | [ | |
比值居民地指数 | RRI | Blue/NIR | [ | |
比值不透水面指数 | RISI | (SWIR_1-SWIR_2)/Blue | [ | |
土壤指数 | 裸土指数 | BSI | ((SWIR_1+Red) -(NIR+Blue))/((SWIR_1+Red) +(NIR+Blue)) | [ |
归一化土壤指数 | NDSI | (SWIR_2-Green)/(SWIR_2+Green) | [ | |
氧化铁比率指数 | IO | Red/Blue | [ | |
燃烧面积指数 | BAI | 1/((0.1-Red)2+(0.06-NIR)2)) | [ |
表4
基于CSD和CDD相结合与CART方法的白洋淀湿地分类精度"
| 2017年CSD+CDD | 2017年CART | 2019年CSD+CDD | 2019年CART | ||||
---|---|---|---|---|---|---|---|---|
用户精度/% | 制图精度/% | 用户精度/% | 制图精度/% | 用户精度/% | 制图精度/% | 用户精度/% | 制图精度/% | |
水体 | 93.37 | 75.52 | 86.05 | 99.02 | 98.96 | 78.77 | 83.02 | 87.04 |
建设用地 | 94.90 | 96.47 | 99.09 | 80.91 | 99.59 | 94.21 | 99.30 | 62.81 |
耕地 | 96.48 | 92.43 | 72.77 | 77.74 | 97.07 | 78.53 | 99.61 | 6.63 |
挺水植被 | 88.42 | 95.21 | 99.39 | 93.22 | 75.74 | 96.97 | 45.66 | 100.00 |
沉水植被 | 71.80 | 86.22 | 83.18 | 86.76 | 72.43 | 88.32 | 0 | 0 |
总体精度/% | 91.32 | 89.65 | 86.97 | 56.21 | ||||
Kappa系数 | 0.88 | 0.86 | 0.83 | 0.37 |
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