【摘要】：獲取農(nóng)作物分類信息是極化合成孔徑雷達(dá)(Polarimetric synthetic aperture radar,PolSAR)的重要應(yīng)用之一,然而單時(shí)相PolSAR數(shù)據(jù)能夠提供的信息十分有限,而且單時(shí)相數(shù)據(jù)的獲取時(shí)間也會(huì)影響農(nóng)作物分類精度。隨著技術(shù)的發(fā)展,出現(xiàn)了大量的機(jī)載和星載PolSAR系統(tǒng),這些系統(tǒng)能夠獲取目標(biāo)重復(fù)觀測的PolSAR數(shù)據(jù),這為多時(shí)相PolSAR的數(shù)據(jù)分析和應(yīng)用提供了可能。本文以多時(shí)相PolSAR農(nóng)作物分類為出發(fā)點(diǎn),通過利用不同農(nóng)作物的極化散射特性的變化特性來提高分類精度。首先,基于極化散射特性分解原理分析了不同農(nóng)作物在生長過程不同時(shí)期所呈現(xiàn)的散射特性變化規(guī)律,在此基礎(chǔ)上定義了一個(gè)新的參數(shù)描述其散射特性的變化特性。其次,基于這一新參數(shù)提出了一種多時(shí)相PolSAR農(nóng)作物監(jiān)督分類算法。最后,通過對(duì)歐洲空間局所提供的基于Radarsat-2實(shí)測仿真生成的Sentinel-1數(shù)據(jù)處理結(jié)果表明,相比于基于復(fù)Wishart分布的監(jiān)督分類算法,農(nóng)作物的整體分類精度提高了約4個(gè)百分點(diǎn),當(dāng)農(nóng)作物種類合并為4類時(shí),整體分類精度提高了約6個(gè)百分點(diǎn)。
[Abstract]:Obtaining crop classification information is one of the important applications of Polarimetric synthetic Aperture Radar (Polarimetric synthetic aperture radar,PolSAR). However, the single phase PolSAR data can provide very limited information, and the acquisition time of the single time phase data will also affect the precision of crop classification. With the development of technology, a large number of airborne and spaceborne PolSAR systems have emerged. These systems can obtain the PolSAR data of target repeated observation, which provides the possibility for the data analysis and application of multitemporal PolSAR. In this paper, the classification accuracy is improved by using the polarization scattering characteristics of different crops as the starting point of multi-phase PolSAR crop classification. Firstly, based on the decomposition principle of polarimetric scattering characteristics, the variation of scattering characteristics of different crops in different periods is analyzed, and a new parameter is defined to describe the variation characteristics of scattering characteristics. Secondly, based on this new parameter, a multi-temporal PolSAR crop classification algorithm is proposed. Finally, the processing results of Sentinel-1 data based on Radarsat-2 simulation provided by the European Space Agency show that compared with the supervised classification algorithm based on complex Wishart distribution, the overall classification accuracy of crops is improved by about 4 percentage points. When the crop species are merged into 4 categories, the overall classification accuracy is improved by about 6 percentage points.
【作者單位】： 西北農(nóng)林科技大學(xué)機(jī)械與電子工程學(xué)院;農(nóng)業(yè)部農(nóng)業(yè)物聯(lián)網(wǎng)重點(diǎn)實(shí)驗(yàn)室;聯(lián)邦科學(xué)與工業(yè)研究組織數(shù)據(jù)處理研究所;
【基金】：國家自然科學(xué)基金項(xiàng)目(41301450、61701416) 衛(wèi)星測繪技術(shù)與應(yīng)用國家測繪地理信息局重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目(KLSMTA-201501)
【分類號(hào)】：S127

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