Object detection and segmentation have recently shown encouraging results toward image analysis and interpretation due to their promising applications in remote sensing image fusion field. Although numerous methods have been proposed, implementing effective and efficient object detection is still very challenging for now, especially for the limitation of single modal data. The use of a single modal data is not always enough to reach proper spectral and spatial resolutions. The rapid expansion in the number and the availability of multi-source data causes new challenges for their effective and efficient processing. In this paper, we propose an effective feature information–interaction visual attention model for multimodal data segmentation and enhancement, which utilizes channel information to weight self-attentive feature maps of different sources, completing extraction, fusion, and enhancement of global semantic features with local contextual information of the object. Additionally, we further propose an adaptively cyclic feature information–interaction model, which adopts branch prediction to decide the number of visual perceptions, accomplishing adaptive fusion of global semantic features and local fine-grained information. Numerous experiments on several benchmarks show that the proposed approach can achieve significant improvements over baseline model.
A Cyclic Information–Interaction Model for Remote Sensing Image Segmentation