Remote sensing crop classification model integrating 1D convolutional residual blocks and temporal attention

Crop type recognition in remote sensing plays a crucial role in agricultural management and resource monitoring. In addressing the challenge of temporal feature extraction from remote sensing time series data, this study introduces a novel temporal extraction module that integrates 1D convolutional residual blocks and temporal attention mechanisms. Our approach effectively captures temporal patterns in crop growth processes and achieves high-precision crop classification in remote sensing imagery. Through experimental validation on the Sentinel2-Agri dataset, our model demonstrates performance improvements in classification accuracy compared to traditional methods, yielding higher accuracy and finer-grained segmentation results. Additionally, ablation studies and exploration of model configurations substantiate the effectiveness and robustness of our approach. This research holds important implications for the advancement of remote sensing-based crop classification.