Change detection aims to identify the difference between dual-temporal images and has garnered considerable attention over the past decade. Recently, deep learning methods have shown robust feature extraction capabilities and have achieved improved detection results; however, they exhibit limitations in preserving clear boundaries for the identified regions, which is attributed to the inadequate contextual information aggregation capabilities of feature extraction, and fail to adequately constrain the delineation of boundaries. To address this issue, a novel dual-branch feature interaction backbone network integrating the CNN and Transformer architectures to extract pixel-level change information was developed. With our method, contextual feature aggregation can be achieved by using a cross-layer feature fusion module, and a dual-branch upsampling module is employed to incorporate both spatial and channel information, enhancing the precision of the identified change areas. In addition, a boundary constraint is incorporated, leveraging an MLP module to consolidate fragmented edge information, which increases the boundary constraints within the change areas and minimizes boundary blurring effectively. Quantitative and qualitative experiments were conducted on three benchmarks, including LEVIR-CD, WHU Building, and the xBD natural disaster dataset. The comprehensive results show the superiority of the proposed method compared with previous approaches.

Hyperspectral images (HSI) contain abundant spectral information, while the spatial resolution is usually limited. To obtain high-spatial-resolution hyperspectral images, various HSI Super-Resolution (SR) methods are proposed. Currently, deep learning based super-resolution reconstruction methods are studied in depth, which take different measures to make full use of the spatial and spectral information of hyperspectral images, and optimize the network with lots of trainings. Although they have achieved satisfying spatial resolution, the spectral consistency before and after reconstruction is difficult to guarantee. In this paper, we proposed a self-attention-based recurrent feedback network for hyperspectral super-resolution reconstruction, utilizing pixel-aware weights and pseudo 3D convolution to enhance the spatial and spectral consistency during the reconstruction process. In addition, group reconstruction is used to reduce the redundancy of information. Spectral consistency regularization is proposed to ensure the spectral consistency before and after reconstruction. The effectiveness of the proposed method is tested on one set of natural image and three hyperspectral remote sensing image datasets