农业工程学报
農業工程學報
농업공정학보
2009年
11期
183-188,封3
,共7页
吴文斌%杨鹏%唐华俊%周清波%Shibasaki Ryosuke%张莉
吳文斌%楊鵬%唐華俊%週清波%Shibasaki Ryosuke%張莉
오문빈%양붕%당화준%주청파%Shibasaki Ryosuke%장리
时间序列分析%滤波%函数%拟合%Savitzky-Golay滤波法%非对称性高斯函数拟合法
時間序列分析%濾波%函數%擬閤%Savitzky-Golay濾波法%非對稱性高斯函數擬閤法
시간서렬분석%려파%함수%의합%Savitzky-Golay려파법%비대칭성고사함수의합법
time series analysis%filtration%functions%fitting%Savitzky-Golay filtration method%Asymmetric Gaussian function fitting method
归一化植被指数(NDVI)时间序列数据拟合目的是降低时序数据的噪声水平,重建高质量的NDVI时序数据,有利于多种参数反演和信息提取.针对国际上普遍应用的两种NDVI时间序列数据拟合方法,即Savitzky-Golay滤波法和非对称性高斯函数拟合法,该文在介绍两种方法基本概念的基础上,利用直接比较法和间接比较法在中国对两种拟合方法进行了比较分析.结果表明,Savitzky-Golay滤波法和非对称性高斯函数拟合法的拟合效果总体上一致,但二者之间还是存在区域差异性,这种区域差异与两种方法的自身特点和中国区域自然条件紧密相关.不同数据拟合方法的比较研究可以弄清每种方法的优缺点和区域适宜性,有助于研究人员针对不同研究目的和研究区域选择适宜的NDVI数据拟合方法,减少遥感数据处理中的误差,提高研究精度.
歸一化植被指數(NDVI)時間序列數據擬閤目的是降低時序數據的譟聲水平,重建高質量的NDVI時序數據,有利于多種參數反縯和信息提取.針對國際上普遍應用的兩種NDVI時間序列數據擬閤方法,即Savitzky-Golay濾波法和非對稱性高斯函數擬閤法,該文在介紹兩種方法基本概唸的基礎上,利用直接比較法和間接比較法在中國對兩種擬閤方法進行瞭比較分析.結果錶明,Savitzky-Golay濾波法和非對稱性高斯函數擬閤法的擬閤效果總體上一緻,但二者之間還是存在區域差異性,這種區域差異與兩種方法的自身特點和中國區域自然條件緊密相關.不同數據擬閤方法的比較研究可以弄清每種方法的優缺點和區域適宜性,有助于研究人員針對不同研究目的和研究區域選擇適宜的NDVI數據擬閤方法,減少遙感數據處理中的誤差,提高研究精度.
귀일화식피지수(NDVI)시간서렬수거의합목적시강저시서수거적조성수평,중건고질량적NDVI시서수거,유리우다충삼수반연화신식제취.침대국제상보편응용적량충NDVI시간서렬수거의합방법,즉Savitzky-Golay려파법화비대칭성고사함수의합법,해문재개소량충방법기본개념적기출상,이용직접비교법화간접비교법재중국대량충의합방법진행료비교분석.결과표명,Savitzky-Golay려파법화비대칭성고사함수의합법적의합효과총체상일치,단이자지간환시존재구역차이성,저충구역차이여량충방법적자신특점화중국구역자연조건긴밀상관.불동수거의합방법적비교연구가이롱청매충방법적우결점화구역괄의성,유조우연구인원침대불동연구목적화연구구역선택괄의적NDVI수거의합방법,감소요감수거처리중적오차,제고연구정도.
The fitting of NDVI time series datasets is to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination, and to reconstruct high-quality NDVI data for parameters inversion and information extraction. A comparative study on two well-known fitting approaches of NDVI time series data, namely, Savitzky-Golay filtering and asymmetric Gaussian function fitting, was described in China using both direct and indirect comparison methods. The results showed that these two methods generally presented a similar performance and a high agreement in data filtering in China, but regional differences existed between them. The main reason for their discrepancies was related to the methodological differences between these two methods, as well as the landscape heterogeneity in different regions. This comparative study can help users to understand the advantages and limitations of each fitting method, and choose the appropriate method to reduce the errors and improve the accuracy in their applications.