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基于遷移學(xué)習(xí)和非監(jiān)督分類的制種玉米遙感識(shí)別方法
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國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2022YFB3903500)


Remote Sensing Identification for Seed Maize with Integrated Migration Learning and Unsupervised Classification
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    摘要:

    作物遙感識(shí)別主要基于監(jiān)督分類方法,對(duì)樣本的數(shù)量、分布要求較高,而農(nóng)作物樣本目視解譯困難。為提高已采集樣本的利用率,同時(shí)降低精細(xì)分類中對(duì)樣本的依賴,本文將遷移學(xué)習(xí)與非監(jiān)督分類方法相結(jié)合,在源域內(nèi)構(gòu)建特征工程,包括:BLUE、GREEN、RED、EDGE1、EDGE2、EDGE3、NIR、SWIR 8個(gè)原始光譜波段,以及NDVI、EVI、RVI、GNDVI、TVI、DVI、MSAVI、GCVI、RNDVI、NDRE、RRI1、RRI2、MSRRE、CLRE、IRECI、LSWI、GCI、SIPI 18個(gè)植被指數(shù),提取出最能表征制種玉米與大田玉米冠層光譜差異,且在不同的源域內(nèi)制種玉米之間差異最小的特征,將其作為先驗(yàn)知識(shí)用于目標(biāo)域的分類任務(wù)中,再基于K-means進(jìn)行制種玉米識(shí)別和制圖。結(jié)果表明,在眾多特征中,近紅外原始波段表現(xiàn)出最強(qiáng)的優(yōu)勢(shì),且在制種玉米母本去雄期后表征效果最好。計(jì)算此時(shí)間段內(nèi)NIR的線性回歸斜率作為特征,相較于直接基于NIR原始波段特征分類精度有所提升。利用K-means方法對(duì)2019年、2020年石河子市和奎屯市的制種玉米分類,2個(gè)目標(biāo)域制種玉米2019年F1值分別為74.35%和64.97%,2020年F1值分別為72.50%和75.69%。本方法通過(guò)提取先驗(yàn)知識(shí),引入非監(jiān)督分類器,有效提高了樣本利用率。通過(guò)提取波段回歸斜率作為特征為原始波段的特征增強(qiáng)提供了思路,同時(shí)也為無(wú)樣本場(chǎng)景下農(nóng)作物精細(xì)分類繪圖提供了方法。

    Abstract:

    Crop classification studies generally focus on different types of crops, while there are fewer studies on the fine classification of different cropping patterns of the same crop. Research on the spatial distribution of seed production is essential to control the maize market, as private seed production and concealment of acreage are occurring in the maize seed market. Seed maize and common maize are the two modes of maize cultivation. Accurate identification of both and remote sensing mapping of the spatial distribution of seed maize are essential for maize seed industry and food security.Traditional crop remote sensing classification methods require a high number and distribution of samples, while visual interpretation of crop samples is difficult. How to improve the utilization of collected samples and at the same time reduce the dependence on samples in fine classification is a pressing issue nowadays. Based on this, combining migration learning with unsupervised classification methods, firstly, using the idea of transfer learning, Linze and Wuwei were used as the source domain, and the feature engineering in the source domain was constructed, including 8 original spectral bands BLUE, GREEN, RED, EDGE1, EDGE2, EDGE3, NIR, SWIR, and 18 vegetation indices NDVI, EVI, RVI, GNDVI, TVI, DVI, MSAVI, GCVI, RNDVI, NDRE, RRI1, RRI2, MSRRE, CLRE, IRECI, LSWI, GCI, SIPI. Then, the features that best characterized the differences in canopy spectra between seed maize and common maize and that differed least between seed maize in different source domains were extracted. Finally, it was used as prior knowledge in an unsupervised classification task in the target domain. The results showed that the near-infrared primordial band exhibited the strongest advantage among the many features. By comparing the classification accuracies of three time-series ranges, namely, before the removal of male ears from the seed maize females, after the removal of male ears from the seed maize females, and during the full-life span of maize growth, the NIR bands were best characterized after the removal of male ears from the seed maize females, i.e., when the DOY was 125~210.In order to further extract the NIR primary band information and enhance the performance of the features, the slope of the linear regression equation in the NIR band of the seed maize female after removal of male ears was used as a feature, and the K-means unsupervised method was used to classify the seed maize in the target domain of Shihezi and Kuitun.After comparative experiments on two target domains in 2019 and 2020, this method mostly showed different degrees of improvement over the K-means classification accuracies characterized by the near-infrared primitive bands in the same time period.In the two target domains Shihezi and Kuitun seed maize had F1 value of 74.35% and 64.97% in 2019 and 72.50% and 75.69% in 2020, respectively.This method effectively improved the utilization of samples by extracting prior knowledge and introducing an unsupervised classifier. It provided ideas for feature enhancement of the primary band by extracting the slope of the band regression as a feature, and provided a method for fine classification mapping of crops in sample-free scenarios.

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常婉秋,姚宇,席曉杰,劉哲,李紹明,張曉東,趙圓圓.基于遷移學(xué)習(xí)和非監(jiān)督分類的制種玉米遙感識(shí)別方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2024,55(8):181-195. CHANG Wanqiu, YAO Yu, XI Xiaojie, LIU Zhe, LI Shaoming, ZHANG Xiaodong, ZHAO Yuanyuan. Remote Sensing Identification for Seed Maize with Integrated Migration Learning and Unsupervised Classification[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(8):181-195.

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  • 收稿日期:2023-11-14
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  • 在線發(fā)布日期: 2024-08-10
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