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Combination of super-resolution reconstruction and SGA-Net for marsh vegetation mapping using multi-resolution multispectral and hyperspectral images

查看全文 作　　者：Bolin [1]Fu;Xidong [1]Sun;Yuyang [1]Li;Zhinan [1]Lao;Tengfang [1]Deng;Hongchang [1]He;Weiwei [2]Sun;Guoqing [1]Zhou 高影响力作者 机构地区：[1]College of Geomatics and Geoinformation,Guilin University of Technology,Guilin,China;[2]Department of Geography and Spatial Information Techniques,Ningbo University,Ningbo,China高影响力机构 出　　处：《International Journal of Digital Earth》索引2023年第16卷第1期,共38页高影响力期刊 基　　金：supported by National Natural Science Foundation of China:[Grant Number 21976043,42122009];Guangxi Science&Technology Program:[Grant Number GuikeAD20159037];‘Ba Gui Scholars’program of the provincial government of Guangxi,and the Guilin University of Technology Foundation:[Grant Number GUTQDJJ2017096];Innovation Project of Guangxi Graduate Education:[Grant Number YCSW2022328]. 摘　　要：Vegetation is crucial for wetland ecosystems.Human activities and climate changes are increasingly threatening wetland ecosystems.Combining satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral bands.This study aimed to propose a method to classify marsh vegetation using multi-resolution multispectral and hyperspectral images,combining super-resolution techniques and a novel self-constructing graph attention neural network(SGA-Net)algorithm.The SGA-Net algorithm includes a decoding layer(SCE-Net)to preciselyfine marsh vegetation classification in Honghe National Nature Reserve,Northeast China.The results indicated that the hyperspectral reconstruction images based on the super-resolution convolutional neural network(SRCNN)obtained higher accuracy with a peak signal-to-noise ratio(PSNR)of 28.87 and structural similarity(SSIM)of 0.76 in spatial quality and root mean squared error(RMSE)of 0.11 and R^(2) of 0.63 in spectral quality.The improvement of classification accuracy(MIoU)by enhanced super-resolution generative adversarial network(ESRGAN)(6.19%)was greater than that of SRCNN(4.33%)and super-resolution generative adversarial network(SRGAN)(3.64%).In most classification schemes,the SGA-Net outperformed DeepLabV3+and SegFormer algorithms for marsh vegetation and achieved the highest F1-score(78.47%).This study demonstrated that collaborative use of super-resolution reconstruction and deep learning is an effective approach for marsh vegetation mapping. 关 键 词：Marsh vegetation classification super-resolution reconstruction SGA-Net and SegFormer multispectral and hyperspectral images spectral restoration spatial resolution improvement
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