【摘要】：小麥?zhǔn)侵袊钪匾霓r(nóng)作物之一,準(zhǔn)確、及時掌握小麥種植面積具有重要意義。以探索合成孔徑雷達(dá)(synthetic aperture radar,SAR)與光學(xué)數(shù)據(jù)對種植結(jié)構(gòu)復(fù)雜地區(qū)冬小麥識別的能力,提高識別精度為目的。該研究以多時相SAR(Sentinel-1A)和光學(xué)影像(Landsat-8)為數(shù)據(jù)源,選取種植結(jié)構(gòu)復(fù)雜的都市農(nóng)業(yè)區(qū)為研究區(qū)。構(gòu)建不同特征向量組合,利用支持向量機(jī)(support vector machine,SVM)提取冬小麥種植面積。通過對比分析基于不同特征向量組合的冬小麥識別精度,結(jié)果表明:1)使用SAR后向散射數(shù)據(jù)得到85.7%的制圖精度和87.9%的用戶精度;2)添加SAR數(shù)據(jù)紋理信息,總體精度高達(dá)90.6%,比單獨(dú)使用后向散射數(shù)據(jù)在制圖精度和用戶精度上分別提高7.6%和6.7%;3)當(dāng)SAR數(shù)據(jù)和光學(xué)影像結(jié)合時,總體精度高達(dá)95.3%(制圖精度97%,用戶精度98.4%),比單獨(dú)使用SAR數(shù)據(jù)在制圖精度和用戶精度上分別提高3.7%和3.8%。因此,基于SAR數(shù)據(jù)的都市農(nóng)業(yè)區(qū)冬小麥分類,有著較高分類精度,紋理信息和光學(xué)影像的添加能有效提高識別精度。研究結(jié)果可為SAR數(shù)據(jù)的農(nóng)作物識別和應(yīng)用提供理論基礎(chǔ)。
[Abstract]:Wheat is one of the most important crops in China. The purpose of this paper is to explore the ability of synthetic Aperture Radar (synthetic aperture radar,SAR) and optical data to identify winter wheat in areas with complex planting structure and to improve the recognition accuracy. In this study, multi-temporal SAR (Sentinel-1A) and optical image (Landsat-8) were used as data sources, and the urban agricultural region with complex planting structure was selected as the study area. Different feature vector combinations were constructed and the planting area of winter wheat was extracted by support vector machine (support vector machine,SVM). By comparing and analyzing the recognition accuracy of winter wheat based on different eigenvector combinations, the results show that: 1) 85.7% mapping accuracy and 87.9% user accuracy are obtained by using SAR backscatter data; 2) adding the texture information of SAR data, the overall accuracy is as high as 90.6, which is 7.6% and 6.7% higher than that of backscatter data used alone in cartographic accuracy and user accuracy, respectively; 3) when SAR data and optical image are combined, the overall accuracy is up to 95.3% (97% for cartography and 98.4% for users), which is 3.7% and 3.8% higher than that of using SAR data alone. Therefore, the classification of winter wheat in urban agricultural area based on SAR data has higher classification accuracy. The addition of texture information and optical image can effectively improve the recognition accuracy. The results can provide a theoretical basis for crop identification and application of SAR data.
【作者單位】： 南京農(nóng)業(yè)大學(xué)資源與環(huán)境科學(xué)學(xué)院;南京農(nóng)業(yè)大學(xué)公共管理學(xué)院;
【基金】：江蘇高校優(yōu)勢學(xué)科建設(shè)工程資助項(xiàng)目(PAPD)
【分類號】：S127;S512.11
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本文編號：2385889