本文選題：Sentinel-A + 時序數(shù)據(jù)��； 參考：《地理與地理信息科學(xué)》2017年05期

【摘要】：Sentinel-2A數(shù)據(jù)具有較高的空間分辨率和時間分辨率,克服了以往時序數(shù)據(jù)難以獲取或空間分辨率低的問題。該文以山西省呂梁市陳家灣流域為研究區(qū),基于Sentinel-2A時序數(shù)據(jù),根據(jù)歸一化植被指數(shù)(NDVI)時序曲線特征和光譜特征,構(gòu)建基于面向?qū)ο鬀Q策樹方法的分層分類模型,成功提取了陳家灣流域的植被信息,分類總體精度達(dá)到89.7%,Kappa系數(shù)為0.87。基于面向?qū)ο鬀Q策樹方法的多時相分類結(jié)果與單時相分類結(jié)果相比,可以有效改善波譜特征相近和受地形影響較大地物的區(qū)分,減少混分現(xiàn)象;基于Sentinel-2A時序數(shù)據(jù)和面向?qū)ο鬀Q策樹分類方法能夠有效提高植被分類的精度。
[Abstract]:The Sentinel-2A data has high spatial resolution and time resolution, which overcomes the problem that the time series data are difficult to obtain or the spatial resolution is low. Taking Chenjiawan Basin of Luliang City, Shanxi Province as the study area, based on Sentinel-2A time series data, based on the characteristics of normalized vegetation index (NDVI) time series curve and spectral features, a hierarchical classification model based on object-oriented decision tree method is constructed. The vegetation information of Chenjiawan watershed was extracted successfully, and the overall classification accuracy was 89.7Kappa coefficient 0.87. Compared with the results of single phase classification, the multi-phase classification based on object-oriented decision tree method can effectively improve the classification of ground objects with similar spectral characteristics and affected by topography, and reduce the mixing phenomenon. Based on Sentinel-2A time series data and object-oriented decision tree classification method, the accuracy of vegetation classification can be improved effectively.
【作者單位】： 中國科學(xué)院遙感與數(shù)字地球研究所/遙感科學(xué)國家重點實驗室;中國科學(xué)院大學(xué);
【基金】：國家高技術(shù)研究發(fā)展計劃(863)項目(2014AA06A511)
【分類號】：TP751

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本文編號：2100262