ConvNeXt-CBAM: integrating CBAM and near-infrared data for improved land cover classification

To address issues such as low classification accuracy caused by small differences in infrared absorption spectra and severe spectral data overlap during land cover classification, this study proposes a land cover classification method based on the ConvNeXt framework, using near-infrared spectral data released by Eurostat as the research object. This method realizes the rapid differentiation of arable land, forest land, and grassland. The method uses short-time Fourier transform preprocessing to convert one-dimensional infrared absorption spectral data into two-dimensional images, optimizes and improves the ConvNeXt Block module, and integrates the CBAM attention module with it to enhance the model’s feature extraction capability and improve model accuracy. Finally, the original activation function GELU is replaced with the PReLU activation function to increase the neural network model’s nonlinear variability, improving model accuracy and efficiency. The results show that this method achieves a land cover classification accuracy of 86.58%, which is 26.60%, 20.25%, 15.38%, 4.72%, and 2.56% higher than common classification models CNN 1, CNN 2, VGG16, ResNet50, and ConvNeXt-t, respectively, verifying its accuracy and reliability in land cover classification, and providing new ideas and methods for land cover classification.