中圖分類號(hào):S812;TP751 文獻(xiàn)標(biāo)識(shí)碼:A 文章編號(hào):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
草原是我國(guó)生態(tài)系統(tǒng)中不可或缺的一部分[1]。我國(guó)的草地面積將近 4×108hm2 ,居世界第二位[2]。然而,自從20世紀(jì)60年代以來(lái),草原退化現(xiàn)象在全國(guó)各地出現(xiàn)上升趨勢(shì)[3],黨的二十大報(bào)告指出,推行草原森林河流湖泊濕地休養(yǎng)生息,提升生態(tài)系統(tǒng)多樣性、穩(wěn)定性、持續(xù)性。全國(guó)第六次荒漠化和沙化土地監(jiān)測(cè)調(diào)查結(jié)果顯示,內(nèi)蒙古荒漠化土地占全國(guó)荒漠化土地面積的 23.04%[4] ,土地沙化、次生鹽漬化嚴(yán)重,生態(tài)修復(fù)的任務(wù)艱巨[5]
草原荒漠化主要表現(xiàn)為植被蓋度(FVC)減少、植被種類減少、裸地面積增加等,因此, FVC 是荒漠化監(jiān)測(cè)的重要指標(biāo)[6。傳統(tǒng)的植被蓋度監(jiān)測(cè)方法以地面實(shí)測(cè)為主,優(yōu)點(diǎn)是精度高、數(shù)據(jù)可靠,但只能在小范圍內(nèi)進(jìn)行植被蓋度計(jì)算[7]。得益于技術(shù)的發(fā)展,衛(wèi)星遙感法可以在大范圍內(nèi)對(duì)植被蓋度進(jìn)行反演,但其分辨率不夠高,成本也比較高昂[8]。近年來(lái),無(wú)人機(jī)(UAV)搭載高光譜儀借助成本低、分辨率高、操作靈活等優(yōu)勢(shì)迅速被大范圍應(yīng)用9,可為荒漠草原植被蓋度的計(jì)算提供技術(shù)支持。
1數(shù)據(jù)采集與預(yù)處理
植被蓋度通常被定義為植被(包括枝、莖、葉)在單位面積內(nèi)的垂直投影面積所占百分比[10]。張燕斌等[11]采用改進(jìn)的3D—ResNet模型對(duì)荒漠草原地物進(jìn)行分類,其總體分類精度為 97.73% ,為荒漠草原整體生態(tài)系統(tǒng)研究奠定基礎(chǔ); Xu 等[12]提出了MSR—3DCNN模型用于高光譜圖像分類,并在3個(gè)開源數(shù)據(jù)集上進(jìn)行試驗(yàn)。深度可分離卷積(DSC)[13]降低了模型參數(shù)量,為深度學(xué)習(xí)模型的輕量化提供新思路。……