Class-Guided Feature Decoupling Network for Airborne Image Segmentation

Contextual information has been demonstrated to be helpful for airborne image segmentation. However, most of the previous works focus on the exploitation of spatially contextual information, which is difficult to segment isolated objects, mainly surrounded by uncorrelated objects. To alleviate this issue, we attempt to take advantage of the co-occurrence relations between different classes of objects in the scene. Especially, similar to other works, convolutional features are first extracted to capture the spatially contextual information. Then, a feature decoupling module is designed to encode the class co-occurrence relations into the convolutional features; thus, the most discriminative features can be decoupled. Finally, the segmentation result is inferred from the decoupled features. The whole process is integrated to form an end-to-end network, named class-guided feature decoupling network (CGFDN). Experimental results on two widely used benchmark data sets show that CGFDN obtains competitive results (>90% overall accuracy (OA) on 5-cmresolution Potsdam and >91% OA on 9-cm-resolution Vaihingen) in comparison with several state-of-the-art models.

Automated summary

This study utilizes both spatial contextual information and co-occurrence relations between different classes of objects to improve airborne image segmentation. A feature decoupling module is designed to encode class co-occurrence relations, enhancing the accuracy of segmentation results. Experimental results demonstrate that the proposed method achieves competitive results on two benchmark data sets.