基于LW一CBAM的荒漠草原植被盖度提取方法研究

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中图分类号：S812；TP751 文献标识码：A 文章编号：2095-5553（2025）07-0111-07

Abstract：Inorder toextractdesert grassland fractionalvegetationcoverage inrealtime，accuratelyandquickly，this paper proposedalightweightnetwork model method integrated atention mechanism（Lightweightnetwork-Convolutional Block Atention Module，LW—CBAM）based on the collected UAV hyperspectral remote sensing data.This method improved the traditional 2Dconvolution kernel to 3Ddeeplyseparableconvolution kernel，andcombined the multi-branch methodand theatention mechanismmoduletomakethe modellightweightandimprovedtheaccuracyof themodel.Inordertoobtain the optimal model，this paperoptimized the batch size and learning rateof the model.Theresultsshowed thatcompared with popular deep learning methods such as ResNet34，VGG16，MobileNetV2andMobileNetV3，LW—CBAM had a higher classification accuracy，OA was 98.97% ， Kappa coefficient was 97.94，and the model had a higher estimation accuracy for fractional vegetation coverage.The absolute error from the true value was only 0.17% .TheLW— CBAM's parameter count was reduced by over 90% compared to the other models，and its computational requirements were respectively 1.37% ， 0.74% ， 13.33% ，and 14.81% of the four other models. During the model validation stage，the estimation error of fractional vegetation coverage by LW—CBAM was below 0.3% . This model provided a feasible method for estimating fractional vegetation coverage in desert steppe and provided a basis for grassland degradation control.

Keywords：fractional vegetation coverage；hyperspectral remote sensing；deep learning；lightweight network; attention mechanism;desert steppe

0 引言

草原是我国生态系统中不可或缺的一部分[1]。（剩余12782字）