EAGNet: A method for automatic extraction of agricultural greenhouses from high spatial resolution remote sensing images based on hybrid multi-attention

The timely and accurate acquisition of greenhouse information is crucial for strategically planning modern agriculture. However, existing methods are affected by the close spacing between agricultural greenhouses, intra-class diversity, and inter-class similarity, resulting in missed and incorrect extraction phenomena. Here, we propose a model for agricultural greenhouse extraction (i.e., EAGNet), which includes a residual block improvement module (RBIM) and boundary segmentation module (BSM) that solve the problem of densely distributed agricultural greenhouse-boundary adhesion. We constructed a class attention module (CAM) to address the leakage extraction phenomenon in agricultural greenhouses caused by intra-class diversity and introduced an object contextual representation module (OCRM) to address the incorrect extraction of agricultural greenhouses caused by the similarity between classes. Experiments on a self-made agricultural greenhouse dataset showed that EAGNet achieved the best extraction results among all compared methods.