对数变换、导数变换的高寒草地反射光谱特征分析与识别——以那曲地区HJ-1A/HSI图像为例  

Spectral Reflectance Characteristics of Alpine Grassland Based on Derivative and Logarithmic Transform Spectra——Take HJ-1A/HSI Images of Naqu Prefecture as an Example

作　　者：刘炜[1] 孙海霞[1] 杨晓波[1] 董建民[1] LIU Wei;SUN Hai-xia;YANG Xiao-bo;DONG Jian-min(Xizang Key Laboratory of Optical Information Processing and Visualization Technology,Xizang Minzu University,Xianyang 712082,China)

机构地区：[1]西藏民族大学西藏光信息处理与可视化技术重点实验室,陕西咸阳712082

出　　处：《光谱学与光谱分析》2020年第7期2200-2207,共8页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(41361044,61162025);西藏自治区自然科学基金项目(XZ2019ZRG-43)资助。

摘　　要：对比3种类型高光谱数据以及2种分类算法,从那曲地区HSI高光谱图像上识别4个草种。结合实地踏勘从HSI高光谱图像上采集藏北嵩草、紫花针茅、高山蒿草和小嵩草这4个草种的原始光谱反射率数据,并分别进行导数变换、对数变换,得到4个草种的原始光谱、一阶导数光谱、对数变换光谱。对这3种光谱数据进行谱线波形分异特征比较、单因素方差分析以及相关分析,从这3种光谱数据中提取出各自适用的敏感谱段,然后将3种光谱数据的敏感谱段分别导入KICA-NFCM算法,通过对HSI图像分类识别出4个草种。对比3种光谱数据各自分类图的识别精度,评价3种光谱数据敏感谱段的适用性;再将3种光谱数据的敏感谱段分别导入ICA-FCM算法,与KICA-NFCM算法分类结果比较对4个草种的识别精度。结果显示谱线波形分异特征比较、单因素方差分析以及相关分析表明,原始光谱、一阶导数光谱、对数变换光谱的敏感谱段分别为788~925, 711~742, 669~682与788~925 nm;使用这3种光谱数据进行KICA-NFCM分类,总体精度、 Kappa系数分别为75.38%, 0.685, 81.26%, 0.752, 87.65%, 0.823;使用3种光谱数据进行ICA-FCM分类,总体精度、 Kappa系数分别为64.39%, 0.569, 67.74%, 0.604, 73.14%, 0.662。比较结果表明对数变换能够增强多组相似光谱数据之问的峰谷特征差异,为通过谱线波形分异特征比较选取敏感谱段创造条件;KICA-NFCM算法可以优化输入特征、并引入加权邻域空间信息计算隶属度函数,针对性解决了标准FCM算法在处理高光谱图像时,目标识别过程受邻域噪声影响,分类图像"椒盐效应"显著、同质区域连通性差的问题。结果表明:应用"对数变换光谱/KICA-NFCM算法"组合能够最准确的从HSI图像上识别4个草种,有效减少混分误判现象,为精准开展高寒草地成像高光谱观测提供技术基础。This paper points out KICA-NFCM algorithm to identify 4 alpine grassland types using HSI hyper-spectral images,by the comparative study of three spectra and two algorithms.Spectral reflectance data for stipa purpurea,kobresia tibetica,little kobresia and kobresia pygmaea was collected from HSI images,based on field investigation and inspection on the spot.Logarithm transformation and derivative transformation were used in the original spectra of 4 alpine grassland types.Sensitivity bands were determined for original spectra data,first-derivative spectra and logarithmic transform spectra,after the application of waveform analysis,one-way ANOV and correlation analysis.Then,sensitivity bands were imported into KICA-NFCM algorithm to identify 4 alpine grassland types mentioned above.For the sake of contrast,ICA-FCM algorithm was tested too.For original spectra data,first-derivative spectra,and logarithmic transform spectra,sensitivity bands were as follows:788~925,711~742,669~682 and 788~925 nm respectively.Based on original spectra data,first-derivative spectra,and logarithmic transform spectra using KICA-NFCM algorithm,overall classification accuracy and KAPPA coefficients were as follows:75.38%,0.685;81.26%,0.752;87.65%,0.823.In contrast,overall classification accuracy and KAPPA coefficients were as follows:64.39%,0.569;67.74%,0.604;73.14%,0.662,based on three types of spectra using ICA-FCM algorithm.Results show that comparing with original spectra data and first-derivative spectra using ICA-FCM algorithm,logarithmic transform spectra using KICA-NFCM algorithm can make a more accurate and efficient identification of 4 alpine grassland types mentioned above,as well as the"salt and pepper noise"was suppressed in classed images.In contrast,ICA-FCM algorithm decreased boundary precision of patch in classed images and region consistency.Using"logarithmic transform spectra/ICA-FCM algorithm"proposed in this paper,the above 4 alpine grassland types in Naqu prefecture can be identified more accuracy.This method provides

关 键 词：成像高光谱 对数变换光谱 导数变换光谱 峰谷特征 敏感谱段 隶属度函数 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]