ZY1-02D AHSI影像归一化阴影植被指数NSVI的波段选择及其构建

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中文摘要：高光谱影像具有连续的地物光谱信息,在阴影检测方面具有巨大的潜力,而波段冗余度高需进行波段优选.归一化阴影植被指数(NSVI)能够扩大光谱差异,在高光谱影像中应用NSVI将更有效地识别阴影.资源一号02D卫星是我国首颗自主研发并成功运行的高光谱业务卫星,数据信噪比大、覆盖能力强,对该高光谱影像进行准确的阴影检测具有重要意义.以ZY1-02DAHSI影像为试验数据,提取并分析明亮区植被、阴影区植被及水体的光谱反射率;结合竞争自适应重加权采样(CARS)和连续投影算法(SPA)筛选能够有效区分典型地物的主要波段,综合考虑算法的特性进一步选出特征波段构建NSVI;通过步长法确定最佳阈值对影像进行分类,从像元值分布情况、分类精度和光谱增强效果等对比出构建NSVI的最佳波段,并结合不同的阴影指数、波段和影像进行综合评价,验证该方法的意义及普适性.结果表明:波段32和波段73是构建NSVI的最佳波段,分别对应红光波段和近红外波段;不同波段构建的NSVI分类精度均高于90％,由最佳波段构建的NSVI分类精度为94.33％,Kappa系数为0.832 8,分类效果最优;NSVI能够增强典型地物间的光谱差异并缓解归一化植被指数的"易饱和"现象,在该影像中因水体累积产生的小波峰有助于提取水体;在ZY1-02DAHSI影像中NSVI的分类效果优于归一化阴影指数和阴影指数,于另一景影像的分类精度也达到93.55％,Kappa系数为0.816 7.由算法筛选出的波段具有一定的代表性,最佳波段构建的NS-VI在ZY1-02D AHSI影像中具有较好的阴影检测能力,对高光谱影像阴影检测及构建植被指数具有一定的借鉴和参考意义.

外文标题：Band Selection and Its Construction for the Normalized Shadow Vegetation Index(NSVI)of ZY1-02D AHSI Image

外文摘要：Hyperspectral images have continuous spectral information of features and have great potential for shadow detection,but high band redundancy requires band preference.Normalized Shaded Vegetation Index(NSVI)can expand the spectral difference,and the application of NSVI in hyperspectral images will identify shadows more effectively.ZY1-02D satellite is the first hyperspectral operational satellite independently developed and successfully operated in China,with a large data signal-to-noise ratio and strong coverage capability,and it is important to perform accurate shadow detection on this hyperspectral image.In this paper,ZY1-02D AHSI images were used as experimental data to extract and analyze the spectral reflectance of vegetation in bright areas,vegetation in shaded areas and water bodies,and Combining Competitive Adaptive Reweighted Sampling(CARS)and Successive Projection Algorithm(SPA)to filter the main wavebands that can effectively distinguish typical features,the characteristics of the algorithms are considered to select the characteristic wavebands further to construct NSVI.The optimal threshold value is determined by the step method to classify the images,and the best band for constructing NSVI is compared in terms of image element value distribution,classification accuracy and spectral enhancement effect.A comprehensive evaluation is made by combining different shadow indices,bands and images to verify the significance and universality of the method in this paper.The results show that band 32 and band 73 are the best bands for NSVI construction,corresponding to the Red band and NIR band,respectively;the classification accuracy of NSVI constructed by different bands is generally higher than 90％,and the classification accuracy of NSVI constructed by the best band is 94.33％with a Kappa coefficient of 0.832 8,which is the best classification effect;NSVI can enhance the spectral difference between typical features and alleviate the"easy saturation"phenomenon of Normalized Difference Vegetation Index,and the small peaks generated by the accumulation of water bodies in this image is helpful to extract water bodies;The classification of NSVI in ZY1-02D AHSI image is better than Normalized Different Umbra Index and Shadow Index,and the classification accuracy in another scene image also reaches 93.55％with a kappa coefficient of 0.816 7;Therefore,the wavebands filtered by the algorithm are representative,and the NSVI constructed by the best waveband has better shadow detection ability in ZY1-02D AHSI images,which has a certain reference and significance for hyperspectral image shadow detection and construction of vegetation index.

外文关键词：

Normalized shaded vegetation index(NSVI)ZY1-02D AHSI imageCompetitive adaptive reweighted sampling(CARS)Successive projection algorithm(SPA)Shadow detection

作者：

许章华、陈玲燕、项颂阳、邓西鹏、李一帆、俞辉、贺安琪、李增禄、郭孝玉

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作者单位：

福州大学地理与生态环境研究院,福建福州 350108

福州大学环境与安全工程学院,福建福州 350108

福建省资源环境监测与可持续经营利用重点实验室,福建三明 365004

空间数据挖掘与信息共享教育部重点实验室,福建福州 350108

福州大学数字中国研究院(福建),福建福州 350108

福建省地质测绘院,福建福州 350108

SEGi University,Kota Damansara 47810,Malaysia

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关键词：

归一化阴影植被指数NSVI ZY1-02D AHSI影像竞争自适应重加权采样(CARS) 连续投影算法(SPA) 阴影检测

基金：

国家自然科学基金面上项目"十四五"国家重点研发计划课题福建省自然科学基金面上项目中国博士后面上基金项目福建省海岛资源生态监测与保护利用重点实验室开放课题福建省资源环境监测与可持续经营利用重点实验室开放课题

项目编号：

420713002023YFD22012052020J015042018M6307282023ZD03ZD202102

出版年：

2024

DOI：

10.3964/j.issn.1000-0593(2024)09-2626-12

光谱学与光谱分析

中国光学学会

光谱学与光谱分析

CSTPCD北大核心

影响因子：0.897

ISSN：1000-0593

年,卷(期)：2024.44(9)