Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping

Moharrami, Meysam; Attarchi, Sara; Gloaguen, Richard; Alavipanah, Seyed Kazem

doi:10.3390/rs16091566

Cited by 4 publications

(1 citation statement)

References 65 publications

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“…High-resolution (HR) remote sensing images, which contain abundant feature and texture information, are widely used in fields such as target detection [1,2], change detection [3,4], semantic segmentation [5,6] and land cover classification [7,8]. However, despite significant advancements in remote sensing technology over the past few decades, challenges still need to be overcome in acquiring HR imagery.…”

Section: Introductionmentioning

confidence: 99%

RMSRGAN: A Real Multispectral Imagery Super-Resolution Reconstruction for Enhancing Ginkgo Biloba Yield Prediction

Fan,

Hu,

Zhao

et al. 2024

Forests

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Multispectral remote sensing data with abundant spectral information can be used to compute vegetation indices to improve the accuracy of Ginkgo biloba yield prediction. The limited spatial resolution of multispectral cameras restricts the detail capture over wide farmland, but super-resolution (SR) reconstruction methods can enhance image quality. However, most existing SR models have been trained on images processed from downsampled high-resolution (HR) images, making them less effective in reconstructing real low-resolution (LR) images. This study proposes a GAN-based super-resolution reconstruction method (RMSRGAN) for multispectral remote sensing images of Ginkgo biloba trees in real scenes. A U-Net-based network is employed instead of the traditional discriminator. Convolutional block attention modules (CBAMs) are incorporated into the Residual-in-Residual Dense Blocks (RRDBs) of the generator and the U-Net of the discriminator to preserve image details and texture features. An unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to capture field multispectral remote sensing images of Ginkgo biloba trees at different spatial resolutions. Four matching HR and LR datasets were created from these images to train RMSRGAN. The proposed model outperforms the traditional models by achieving superior results in both quantitative evaluation metrics (peak signal-to-noise ratio (PSNR) is 32.490, 31.085, 27.084, 26.819, and structural similarity index (SSIM) is 0.894, 0.881, 0.832, 0.818, respectively) and qualitative evaluation visualization. Furthermore, the efficiency of our proposed method was tested by generating individual vegetation indices (VIs) from images taken before and after reconstruction to predict the yield of Ginkgo biloba. The results show that the SR images exhibit better R2 and RMSE values than LR images. These findings show that RMSRGAN can improve the spatial resolution of real multispectral images, increasing the accuracy of Ginkgo biloba yield prediction and providing more effective and accurate data support for crop management.

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