【摘要】：在多云多雨的地區(qū),光學(xué)遙感存在著獲取無(wú)云數(shù)據(jù)困難的難題,這會(huì)導(dǎo)致時(shí)間序列應(yīng)用中可用數(shù)據(jù)匱乏。因此,本文面向稀疏時(shí)間序列遙感數(shù)據(jù),根據(jù)噪聲造成遙感影像上歸一化差分植被指數(shù)(NDVI)被低估的事實(shí),提出了一種知識(shí)引導(dǎo)的擬合方法。首先,在遙感影像預(yù)處理的基礎(chǔ)上,利用先驗(yàn)知識(shí)和時(shí)序差分法對(duì)噪聲進(jìn)行識(shí)別和剔除;然后,采用高斯二階模型對(duì)原始數(shù)據(jù)進(jìn)行擬合;最后,根據(jù)擬合殘差更新權(quán)重,進(jìn)行迭代擬合,重復(fù)上述過(guò)程直至獲得穩(wěn)定的結(jié)果。本文以Landsat 8 OLI作為數(shù)據(jù)源,對(duì)浙江省杭州地區(qū)的森林?jǐn)?shù)據(jù)進(jìn)行擬合,結(jié)果表明:在稀疏時(shí)間序列數(shù)據(jù)的情況下,本文方法與MODIS數(shù)據(jù)擬合結(jié)果的相關(guān)系數(shù)達(dá)到0.92,關(guān)鍵時(shí)點(diǎn)(如NDVI峰值點(diǎn)等)的時(shí)間誤差在5 d;相比當(dāng)前主流方法的0.88與8 d具有更高的精度。
[Abstract]:In cloudy and rainy areas, optical remote sensing is difficult to obtain cloudless data, which leads to the lack of available data in time series applications. Therefore, in this paper, for sparse time series remote sensing data, according to the fact that the normalized differential vegetation index (NDVI) is underestimated in remote sensing images caused by noise, a knowledge guided fitting method is proposed. First, on the basis of remote sensing image preprocessing, the prior knowledge and time-series difference method are used to identify and eliminate the noise. Then, the Gao Si second-order model is used to fit the original data. Finally, the weight is updated according to the fitting residual error. Iterative fitting is carried out and the process is repeated until a stable result is obtained. In this paper, Landsat 8 OLI is used as the data source to fit the forest data in Hangzhou area of Zhejiang Province. The results show that in the case of sparse time series data, The correlation coefficient between this method and the fitting results of MODIS data is 0.92.The time error of critical time points (such as NDVI peak) is 5 days, which is higher than that of the current mainstream methods 0.88 and 8 days.
【作者單位】： 浙江工業(yè)大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(編號(hào):61572437,41301473)~~
【分類號(hào)】：TP751

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