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基于改進TransUNet的黃土高原梯田作業(yè)區(qū)域提取方法
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國家重點研發(fā)計劃項目(2022YFD2001300,、2023YFD1000800)和陜西省重點研發(fā)計劃項目(2022ZDLNY03-04)


Extraction Method of Terrace Operation Area in Loess Plateau Based on Improved TransUNet
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    摘要:

    農(nóng)田作業(yè)區(qū)域地圖準確構建是實現(xiàn)農(nóng)機路徑規(guī)劃和導航作業(yè)的重要前提。黃土高原梯田田塊大小各異,、形狀復雜多變,,并且存在部分凹坑、溝坎和諸多危險作業(yè)邊界,,常用的衛(wèi)星測點等方法難以準確地提取梯田作業(yè)區(qū)域,,本文以無人機梯田遙感圖像為數(shù)據(jù)基礎,提出一種基于多尺度特征提取與融合上采樣的改進TransUNet模型,。在編碼器部分,,通過引入金字塔壓縮注意力模塊(Pyramid squeeze attention, PSA),在通道注意力的基礎上增強對不同尺度梯田特征提取和融合的能力,,并使用殘差結構優(yōu)化Transformer層,;在解碼器部分,引入Dual up-sample模塊將亞像素卷積層與雙線性插值上采樣兩者融合,,提升梯田邊界分割精度的同時防止棋盤效應,,并在解碼器末尾添加通道和空間注意力機制模塊(Concurrent spatial and channel squeeze and channel excitation, SCSE),,同時對空間和通道維度的信息進行整合增強,有助于圖像細節(jié)特征逐步恢復,。實驗結果表明,,改進TransUNet模型在直長條形、蜿蜒長條形和不規(guī)則形3類典型梯田測試集上平均像素準確率,、F1值和平均交并比平均分別達96.0%,、96.0%和92.3%,3項指標相較于改進前平均提升1.8個百分點,,與代表性的PSPNet,、HRNet V2、DeepLab V3+,、U-Net模型相比,,3項指標平均提升8.3、6.2,、5.0,、4.2個百分點。在3類單塊梯田測試集上,,本文模型表現(xiàn)最優(yōu),,像素交并比平均可達97.0%。本文方法可為黃土高原梯田環(huán)境地圖構建和丘陵山地農(nóng)機導航作業(yè)提供參考,。

    Abstract:

    Accurate map construction of farmland operation area is an important prerequisite for realizing the path planning and navigation operation of farm machinery. The terraced fields on the Loess Plateau have different sizes and complex shapes, and there are some pits, ditches and many dangerous operation boundaries, so it is difficult to accurately extract the terraced operation area by the commonly used satellite point measurement methods. An improved TransUNet model based on multi-scale feature extraction and fusion up-sampling was proposed with the remote sensing images of terraced fields from UAVs as the data base.In the encoder part, the ability of feature extraction and fusion for different scales of terraces was enhanced by introducing the pyramid squeeze attention (PSA) module on top of the channel attention and the Transformer layer was optimized by using the residual structure.In the decoder part, the Dual up-sample module was introduced to integrate the sub-pixel convolutional layer with the bilinear interpolation upsampling to improve the accuracy of the terraced field boundary segmentation while preventing the checker board effect, and the channel and spatial attention mechanism module (concurrent spatial and channel squeeze and channel excitation (SCSE)) was added at the end of the decoder to integrate and enhance the information of spatial and channel dimensions, which helped to recover the detailed features of the image step by step.The experimental results showed that the mean pixel accuracy, F1 value, and mean intersection over union of the improved TransUNet model can reach up to 96.0%, 96.0%, and 92.3% on average on the test set of three typical terraces, namely, straight and long stripes, meandering stripes, and irregular shapes, respectively, which was an average enhancement of 1.8 percentage points compared with the pre-improvement period, and compared with the representative PSPNet, HRNet V2, DeepLab V3+, and U-Net models, the average improvement of the three indicators was 8.3, 6.2, 5.0, and 4.2 percentage points. On the test set of three types of single terraces, the proposed model performed the best, and intersection over union can reach 97.0% on average. The method can provide a reference for the construction of terraced field environment maps in the Loess Plateau and the navigation operation of agricultural machinery in hilly and mountainous areas.

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楊福增,袁敏鑫,許翔虎,王旺,楊江濤,劉志杰.基于改進TransUNet的黃土高原梯田作業(yè)區(qū)域提取方法[J].農(nóng)業(yè)機械學報,2024,55(12):278-286. YANG Fuzeng, YUAN Minxin, XU Xianghu, WANG Wang, YANG Jiangtao, LIU Zhijie. Extraction Method of Terrace Operation Area in Loess Plateau Based on Improved TransUNet[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(12):278-286.

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  • 收稿日期:2024-01-16
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  • 在線發(fā)布日期: 2024-12-10
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