A Multispectral Feature Selection Method Based on a Dual-Attention Network for the Accurate Estimation of Fractional Vegetation Cover in Winter Wheat

Abstract

Spectral information plays a crucial role in fractional vegetation cover (FVC) estimation, and selecting the appropriate spectral information is essential for improving the accuracy of FVC estimation. Traditionally, spectral feature selection is primarily guided by physical mechanisms or empirical statistical models. This has led to the use of multispectral and hyperspectral images, which often result in missing or redundant information, thereby decreasing the efficiency and accuracy of FVC estimation. This study proposes a novel dual-attention network to select the feature bands of Sentinel-2 multispectral images for the accurate FVC estimation of winter wheat. In the first step, the importance of hyperspectral band reflectances was determined using simulated data from the PROSAIL model, by combining the dual-attention mechanism with the convolutional neural network (DAM-CNN). In the second step, the importance of Sentinel-2 multispectral bands was converted from the hyperspectral band importance identified in the previous stage, and subsequently ranked accordingly. Based on the feature ranking results, multispectral simulated data translated from hyperspectral simulated data were used for CNN training, and multispectral feature selection was conducted based on FVC accuracy. Finally, the selected features were assessed based on their performance in FVC estimation using a CNN model with real data. The experimental results indicate that during the key growth period of winter wheat, the combination of red, green, and red-edge bands significantly influences the FVC estimation accuracy. Band 3 (Green), band 4 (Red), band 5 (Red-edge 1), and band 6 (Red-edge 2) of Sentinel-2 satellite images contribute most significantly to winter wheat FVC estimation, achieving an accuracy comparable to that obtained using all bands, while reducing the training time by 19.1%, as confirmed by field survey data.

Keywords

• fractional vegetation cover
• dual-attention mechanism
• convolutional neural network
• feature selection
• feature assessment