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

作     者：Bolin Fu Xidong Sun Yuyang Li Zhinan Lao Tengfang Deng Hongchang He Weiwei Sun Guoqing Zhou 

作者机构：College of Geomatics and GeoinformationGuilin University of TechnologyGuilinChina Department of Geography and Spatial Information TechniquesNingbo UniversityNingboChina 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2023年第16卷第1期

页      面：2724-2761页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

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] 

主　　题：Marsh vegetation classification super-resolution reconstruction SGA-Net and SegFormer multispectral and hyperspectral images spectral restoration spatial resolution improvement 

摘      要：Vegetation is crucial for wetland *** activities and climate changes are increasingly threatening wetland *** satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral *** 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)*** SGA-Net algorithm includes a decoding layer(SCE-Net)to preciselyfine marsh vegetation classification in Honghe National Nature Reserve,Northeast *** 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 *** 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.