With the development of earth observation technology, hyperspectral image (HSI) and light detection and ranging (LiDAR) data collaborative monitoring has shown great potential in the ecological protection and restoration of coastal wetlands. However, due to the different working principle adopted by the HSI sensor and LiDAR sensor, the data obtained by them has different distribution characteristics. The distribution difference limits the fusion of HSI and LiDAR data, bringing a great challenge for coastal wetland classification. To tackle this problem, a multi-source feature embedding and interaction fusion network is proposed for coastal wetland classification, named MsFE-IFN. First, the HSI and LiDAR data are embedded in the same feature space, where the feature distribution of multi-source remote sensing are aligned to alleviate data distribution differences. Second, the aligned HSI and LiDAR features interact information in channels and pixels, which is able to establish the relationship of spectral, elevation and geospatial. Third, the HSI and LiDAR feature are sent into the feature fusion network, in which the low-frequency residual is retained to enrich intra-class features. Finally, the fused feature is applied for final class prediction. Experiments conducted on three coastal wetland HSI-LiDAR datasets created by ourselves demonstrate the superiority of the proposed MsFE-IFN for coastal wetland classification. The codes will be available from the website:https://github.com/bigshot-g/IEEE_TGRS_MsFE-IFN.

Image and sentence matching has attracted increasing attention since it is associated with two important modalities of vision and language. Previous methods aim to find the latent correspondences between image regions and words by aggregating the similarities of the region-word pairs. However, these approaches consider little about the relationships of diverse regions in the image and treat the similarities of all region-word pairs equally. Moreover, focusing on fine-grained alignment overly, the true meaning of the original image will be likely distorted. In this paper, a novel Region Reinforcement Network with Topic Constraint (RRTC) is proposed to explore the correspondences between images and texts. Specifically, the region reinforcement network is built to infer fine-grained correspondence by considering the relationships of regions and re-assigning region-word similarities. Meanwhile, the topic constraint module is presented to summarize the central theme of images, which constrains the original image deviation. Extensive experimental results on MSCOCO and Flickr30k datasets verify the effectiveness of our proposed RRTC.