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考慮光譜變異性的多光譜植被識別最優(yōu)特征空間構(gòu)建
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國家自然科學(xué)基金項目(42201376、41771449)、中央高?;究蒲袠I(yè)務(wù)費專項資金項目(2022-4-ZD-05、2023-3-YB-12)和同濟大學(xué)“中德合作2.0”培育項目(4300143344/039)


Optimal Feature Space Construction for Multispectral Vegetation Recognition Considering Endmember Variability
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    摘要:

    在中低分辨率遙感衛(wèi)星影像上,植被識別受數(shù)據(jù)獲取條件和不同生長期等因素的影響,會存在端元光譜變異現(xiàn)象,導(dǎo)致植被解混誤差較大。提出了一種顧及端元光譜變異性的最佳距離遺傳算法(IIDGA),通過自動特征選擇方法減小端元類內(nèi)差異,增大類間差異,構(gòu)建適用于中等分辨率影像的植被解混最優(yōu)特征空間,提高Landsat影像的植被識別精度。通過比較傳統(tǒng)波段組合、光譜和紋理特征全集與IIDGA優(yōu)選特征的線性解混模型效果,驗證了最優(yōu)特征選擇的重要性。結(jié)果顯示,特征選擇有助于提升解混精度(IIDGA的均方根誤差最低,為0.180);同時,通過比較基于IID指數(shù)的Filter算法、基于標(biāo)準(zhǔn)GA的Wrapper算法和IIDGA在最優(yōu)特征自動選取方面的性能,證實了IIDGA在平衡精度與效率方面的優(yōu)勢。

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    Due to differences in data acquisition and vegetation growth periods, vegetation recognition on low-and medium-resolution remote sensing imagery widely suffers from endmember variability. The endmember variability directly leads to large vegetation unmixing errors. To increase the vegetation recognition accuracy on the multispectral imagery, an intra-inter distance genetic algorithm (IIDGA) that accounts for the endmember variability was proposed. IIDGA can decrease the intra-distance and increase the inter-distance simultaneously, which enhanced the distinguishability of the endmembers through an automatic feature selection method. An optimal feature space for vegetation unmixing was constructed on the medium resolution imagery to improve the vegetation recognition accuracy based on the Landsat imagery. The importance of optimal feature selection was demonstrated by comparing the linear unmixing model accuracy based on the classical band combination features, the spectral and textural feature set and the proposed IIDGA. The results verified that feature selection was beneficial to improve the unmixing accuracy. The RMSE of IIDGA equalled 0.180 which was the lowest among the three methods. Meanwhile, the IID index-based Filter method, the standard GA-based Wrapper method and the proposed method were compared with their performances in automatic optimal feature selection. The results confirmed the superiority of the IIDGA in trading off accuracy and efficiency.

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林怡,厲朗,宇潔,高忱,鐘代琪,陳鑫,楊羽軒.考慮光譜變異性的多光譜植被識別最優(yōu)特征空間構(gòu)建[J].農(nóng)業(yè)機械學(xué)報,2024,55(8):225-232. LIN Yi, LI Lang, YU Jie, GAO Chen, ZHONG Daiqi, CHEN Xin, YANG Yuxuan. Optimal Feature Space Construction for Multispectral Vegetation Recognition Considering Endmember Variability[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(8):225-232.

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  • 收稿日期:2024-04-26
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  • 在線發(fā)布日期: 2024-08-10
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