Prototype network-based methods have made substantial progress in few-shot relation extraction (FSRE) by enhancing relation prototypes with relation descriptions. However, the distribution of relations and instances in distinct representation spaces isolates the constraints of relations on instances, making relation prototypes biased. In this paper, we propose an end-to-end partial order-centered hyperbolic representation learning (PO-HRL) framework, which imposes the constraints of relations on instances by modeling partial order in hyperbolic space, so as to effectively learn the distribution of instance representations. Specifically, we develop the hyperbolic supervised contrastive learning based on Lorentzian cosine similarity to align representations of relations and instances, and model the partial order by constraining instances to reside within the Lorentzian entailment cone of their respective relation. Experiments on three benchmark datasets show that PO-HRL outperforms the strong baselines, especially in 1-shot settings lacking relation descriptions.

An extended GIHS (EGIHS) fusion approach is presented for merging the panchromatic (PAN) image and the multi-spectral (MS) image with model-based optimization (MBO). The pan-sharpened MS image is posed as the optimization solution of the proposed functional, which consists of two energy terms. The first energy term injects the details of the PAN image into the MS image with the generalized IHS (GIHS) fusion model and the second energy term preserves the spectral contents of the MS image by enforcing the low-pass version of the pan-sharpened image should be close to the original up-sampled MS image, in which the low-pass filter is developed based on the modulation transfer function (MTF) of the different MS band. The pan-sharpened MS images are decoupled in the proposed method, which allows an efficient implementation. Experiments on IKONOS and QuickBird datasets demonstrate that the proposed approach outperforms the state-of-the-art fusion models including IHS-based GIHS, GIHSA, IHS-BT, MBO-based Adaptive IHS and MRA-based AWLP.

A novel urban land-use/land-cover classification framework for very high resolution SAR images which combines scene description and scene-based mapping is presented in this paper. We use textural features for scene region description, including the traditional GLCM feature and the new scene scattering descriptor. The scene scattering descriptor is proposed in this paper to model the scattering behavior and context appearance of urban scenes. This work intends to implement an efficient description of urban land covers from the urban backscattering behavior perspective and apply it to scene-based mapping of urban areas. The framework demonstrates good performance in both accuracy and efficiency for urban scene interpretation of very high resolution SAR images.

A new pan-sharpening approach is proposed for merging the panchromatic image and the multi-spectral image by jointly using IHS and variational method. The substitute image of the intensity component of IHS transform is the optimization solution of the proposed energy functional. The functional consists of three energy terms. The first term extracts the spatial details from the Pan image and injects the details into the MS image. The second term preserves the spectral signature of the MS image by requiring that the low-resolution version of the substitute image should be close to the intensity component. The third term guarantees the smoothness of the optimization solution. The proposed method is validated on the IKONOS and QuickBird dataset. In comparison with the fusion models including extended fast IHS (eFIHS), AWLP and IHS-BT, the prospective fusion results are presented by the proposed model.