F-Segfomer: A Feature-Selection Approach for Land Resource Management on Unseen Domains

Satellite imagery segmentation is essential for effective land resource management. However, diverse geographical landscapes may limit segmentation accuracy in practical applications. To address these challenges, we propose the F-Segformer network, which incorporates a Variational Information Bottleneck (VIB) module to enhance feature selection within the SegFormer architecture. The VIB module serves as a feature selector, providing improved regularization, while SegFormer is well adapted to unseen domains. Combining these methods, our F-Segformer robustly enhanced segmentation performance in new regions that do not appear in the training process. Additionally, we employ Online Hard Example Mining (OHEM) to prioritize challenging samples during training, the setting helps with accelerating model convergence even with the co-trained VIB loss. Experimental results on the LoveDA dataset show that our method can achieve a comparable result to well-known domain-adaptation methods without using data from the target domain. In a practical scenario when the segmentation model is trained on a domain and tested on an unseen domain, our method shows a significant improvement. Last but not least, OHME helps the model converge three times faster than without OHME.