Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the deep features of cropland, the accumulation of irrelevant features and the loss of key features will lead to poor detection results. To effectively solve this problem, a novel cropland change detection network (CroplandCDNet) is proposed in this paper; this network combines an adaptive receptive field and multiscale feature transmission fusion to achieve accurate detection of cropland change information. CroplandCDNet first effectively extracts the multiscale features of cropland from bitemporal remote sensing images through the feature extraction module and subsequently embeds the receptive field adaptive SK attention (SKA) module to emphasize cropland change. Moreover, the SKA module effectively uses spatial context information for the dynamic adjustment of the convolution kernel size of cropland features at different scales. Finally, multiscale features and difference features are transmitted and fused layer by layer to obtain the content of cropland change. In the experiments, the proposed method is compared with six advanced change detection methods using the cropland change detection dataset (CLCD). The experimental results show that CroplandCDNet achieves the best F1 and OA at 76.04% and 94.47%, respectively. Its precision and recall are second best of all models at 76.46% and 75.63%, respectively. Moreover, a generalization experiment was carried out using the Jilin-1 dataset, which effectively verified the reliability of CroplandCDNet in cropland change detection.

Deep learning techniques have become the mainstream approach for fine-grained crop classification in unmanned aerial vehicle (UAV) remote sensing imagery. However, a significant challenge lies in the long-tailed distribution of crop samples. This imbalance causes neural networks to focus disproportionately on majority class features during training, leading to biased decision boundaries and weakening model performance. We designed a minority sample enhanced sampling (MES) method with the goal of addressing the performance limitations that are caused by class imbalance in many crop classification models. The main principle of MES is to relate the re-sampling probability of each class to the sample pixel frequency, thereby achieving intensive re-sampling of minority classes and balancing the training sample distribution. Meanwhile, during re-sampling, data augmentation is performed on the sampled images to improve the generalization. MES is simple to implement, is highly adaptable, and can serve as a general-purpose sampler for semantic segmentation tasks, functioning as a plug-and-play component within network models. To validate the applicability of MES, experiments were conducted on four classic semantic segmentation networks. The results showed that MES achieved mIoU improvements of +1.54%, +4.14%, +2.44%, and +7.08% on the Dali dataset and +2.36%, +0.86%, +4.26%, and +2.75% on the Barley Remote Sensing Dataset compared with the respective benchmark models. Additionally, our hyperparameter sensitivity analysis confirmed the stability and reliability of the method. MES mitigates the impact of class imbalance on network performance, which facilitates the practical application of deep learning in fine-grained crop classification.