With the development of deep learning, significant progress has been made in change detection (CD) methods for remote sensing (RS) images. However, many convolutional neural network (CNN)-based methods are constrained in capturing long-range dependencies due to the limitations of the receptive field. Transformers rely on self-attention mechanisms to effectively achieve global information modeling and are widely used in CD tasks. Nevertheless, transformer-based CD methods still suffer from issues such as pseudochanges and incomplete edges due to the lack of position and temporal correlations in bitemporal RS images. To deal with this issue, we propose a position-temporal awareness transformer (PT-Former), which models position and temporal relations in bitemporal images. Specifically, a Siamese network attached to a position-aware embedding module (PEM) serves as a feature encoder to extract the features of changed areas. Then, a temporal difference perception module (TDPM) is designed to capture the cross-temporal shift and enhance the difference perception ability during cross-temporal interaction. Meanwhile, the contextual information of the ground object is aggregated by the fusion block, and the spatial relation is reconstructed under the guidance of bitemporal features. The experimental results validate the superiority of PT-Former on three benchmark datasets, including the season-varying CD (SVCD) dataset, the learning vision and RS laboratory building CD (LEVIR-CD) dataset, and the WHU-CD dataset confirming the potential of PT-Former for CD tasks in RS images. The code will be available at https://github.com/liuyk29/PT-Former .

Although some image retrieval methods were proposed to accelerate finger vein recognition, the insufficient feature (e.g., the number of vein point) and unfavorable encoding (e.g., predefined threshold based binarization) limited retrieval performance largely. In view of this problem, we develop a new retrieval framework, based on the integration of discriminative texture features and similarity-preserving binary codes. In detail, the vector and scalar features, measuring the gray level, gray difference, and gray gathering of image patch, are both used to represent finger vein image. And to improve the retrieval efficiency, the high-dimensional decimal features are further encoded into the compact binary patterns by principal component analysis (PCA) and similarity-preserving iterative quantization (ITQ). Experimental results on one large finger vein database prove that the proposed method can powerfully improve the retrieval accuracy and efficiency.