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

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 precisely fine 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 R2 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.