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基于多模態(tài)多目標(biāo)優(yōu)化的端元束提取方法研究
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國家重點(diǎn)研發(fā)計劃項(xiàng)目(2022YFD2001405)、自然資源部超大城市自然資源時空大數(shù)據(jù)分析應(yīng)用重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目(KFKT-2022-05)、浙江省農(nóng)業(yè)智能裝備與機(jī)器人重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目(2023ZJZD2306)、自然資源部城市國土資源監(jiān)測與仿真重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目(KF-2021-06-115)、深圳市科技計劃項(xiàng)目(ZDSYS20210623091808026)、北京航空航天大學(xué)虛擬現(xiàn)實(shí)技術(shù)與系統(tǒng)全國重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目(VRLAB2022C10)、能源清潔利用國家重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目(ZJUCEU2022002)、中國農(nóng)業(yè)大學(xué)2115人才工程項(xiàng)目、中央高?;究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金項(xiàng)目和中國農(nóng)業(yè)大學(xué)研究生自主創(chuàng)新研究基金項(xiàng)目(2022TC169)


Endmember Bundle Extraction Method Based on Multi-modal and Multi-objective Optimization
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    摘要:

    為解決高光譜影像受傳感器及分辨率的影響所產(chǎn)生的光譜變化給解混造成的困擾,提出基于多模態(tài)多目標(biāo)優(yōu)化的端元束提取方法(MOPSOSCD)。對高光譜圖像進(jìn)行標(biāo)號編碼,采用基于索引的環(huán)形拓?fù)浣Y(jié)構(gòu)進(jìn)行鄰域的個體交互,通過鄰域最優(yōu)改進(jìn)粒子群速度更新方式并整數(shù)化粒子位置更新。同時,根據(jù)高光譜圖像空間特征,通過改進(jìn)決策空間擁擠距離提高決策空間的多樣性,再結(jié)合目標(biāo)空間的擁擠距離進(jìn)行綜合排序,實(shí)現(xiàn)多模態(tài)多目標(biāo)優(yōu)化的粒子篩選。當(dāng)粒子定向移動概率pm為0.2、粒子數(shù)P為30及迭代次數(shù)M為400時,算法在MUUFL數(shù)據(jù)集上均方根誤差(RMSE)及平均光譜角距離(mSAD)分別為0.0088、0.1112。通過對比試驗(yàn),本文方法相較于VCA、DPSO等方法具有更高的提取精度和效率,為高光譜解混提供了更加準(zhǔn)確的端元束提取方法。

    Abstract:

    Hyperspectral image has continuous spectral information of ground objects, which is an essential means of remote sensing monitoring. On this basis, the endmembers of the features can be extracted by decomposing the mixed pixel spectrum and exploring the degree of each endmember participates in the mixing. However, specific spectral changes cause trouble for spectral unmixing due to the sensor and the image’s resolution. To solve this problem, an endmember bundle extraction method based on multi-modal and multi-objective particle swarm optimization by special crowding distance (MOPSOSCD) was proposed. Firstly, for a three-dimensional hyperspectral image, the label coding was carried out pixel by pixel, and the index-based ring topology was used for individual interaction in different neighborhoods. Secondly, for particle velocity and position update, the position update method of PSO was adopted and the particle swarm velocity update method and the integer particle position update were improved through neighborhood optimization. The objective function selection was measured by two RMSEs, that was, the unconstrained least squares method was used to solve the RMSE of the abundance map anti-mixing and the original map, and the fully constrained least squares method was used to solve the RMSE of the abundance map anti-mixing and the original map. At the same time, according to the spatial characteristics of hyperspectral images, decision space diversity was improved by improving the crowded distance of decision space. Finally, the crowding distances of the decision space and the target space were combined and sorted, and the particles were updated according to the sorting results. When the particle directional movement probability was 0.2, the number of particles was 30, and the number of iterations was 400, the results of RMSE and mSAD on the MUUFL dataset were 0.0088 and 0.1112, respectively. Through the comparative test, the method had higher extraction accuracy and efficiency than VCA and DPSO, providing a more accurate end beam extraction method for hyperspectral unmixing.

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林潔雯,陳建,羅婷文,徐志搏.基于多模態(tài)多目標(biāo)優(yōu)化的端元束提取方法研究[J].農(nóng)業(yè)機(jī)械學(xué)報,2023,54(7):234-242. LIN Jiewen, CHEN Jian, LUO Tingwen, XU Zhibo. Endmember Bundle Extraction Method Based on Multi-modal and Multi-objective Optimization[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(7):234-242.

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  • 收稿日期:2022-11-18
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  • 在線發(fā)布日期: 2023-07-10
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