In the theory of belief functions, the approximation of a basic belief assignment (BBA) is for reducing the high computational cost especially when large number of focal elements are available. In traditional BBA approximation approaches, a focal element’s own characteristics such as the mass assignment and the cardinality, are usually used separately or jointly as criteria for the removal of focal elements. Besides the computational cost, the distance between the original BBA and the approximated one is also concerned, which represents the loss of information in BBA approximation. In this paper, an iterative approximation approach is proposed based on maximizing the closeness, i.e., minimizing the distance between the approximated BBA in current iteration and the BBA obtained in the previous iteration, where one focal element is removed in each iteration. The iteration stops when the desired number of focal elements is reached. The performance evaluation approaches for BBA approximations are also discussed and used to compare and evaluate traditional BBA approximations and the newly proposed one in this paper, which include traditional time-based way, closeness-based way and new proposed ones. Experimental results and related analyses are provided to show the rationality and efficiency of our proposed new BBA approximation.

Image registration is a crucial and fundamental problem in image processing and computer vision, which aims to align two or more images of the same scene acquired from different views or at different times. In image registration, since different keypoints (e.g., corners) or similarity measures might lead to different registration results, the selection of keypoint detection algorithms or similarity measures would bring uncertainty. These different keypoint detectors or similarity measures have their own pros and cons and can be jointly used to expect a better registration result. In this paper, the uncertainty caused by the selection of keypoint detector or similarity measure is addressed using the theory of belief functions, and image information at different levels are jointly used to achieve a more accurate image registration. Experimental results and related analyses show that our proposed algorithm can achieve more precise image registration results compared to several prevailing algorithms.

Dempster–Shafer evidence theory (DST) is an important tool for uncertainty modeling and reasoning, where the uncertainty reasoning includes both the evidence combination and conditioning. New conditioning rules for evidence updating in DST are proposed in this paper. First, two new definitions, namely the weak conditional basic belief assignment (BBA) and strong conditional BBA, are proposed in the spirit of conditional probability in the probabilistic framework. Then, the corresponding Jeffrey-like conditioning rules are proposed to update evidence. The proposed methods have some desirable properties for the evidence updating. Some numerical examples are provided, where existing conditioning rules in DST are compared with newly proposed methods. Experimental results and related analyses show that the conditional BBAs and Jeffrey-like rules proposed in this paper are rational and effective.

Hyperspectral image (HSI) contains abundant spectral-spatial information, which is widely used in many fields. HSI classification is a fundamental and important task, which aims to assign each pixel a specific class label. However, the high spectral variability and the limited labeled samples create challenges for HSI classification, which results in poor data separability and makes it difficult to learn highly discriminative semantic features. In order to address the above problems, a novel spectral-spatial center-aware bottleneck Transformer is proposed. First, the highly relevant spectral information and the complementary spatial information at different scales are integrated to reduce the impact caused by the high spectral variability and enhance the HSI’s separability. Then, the feature correction layer is designed to model the cross-channel interactions, thereby promoting the effective cooperation between different channels to enhance overall feature representation capability. Finally, the center-aware self-attention is constructed to model the spatial long-range interactions and focus more on the neighboring pixels that have relatively consistent spectral-spatial properties with the central pixel. Experimental results on the common datasets show that compared with the state-of-the-art classification methods, S2CABT has the better classification performance and robustness, which achieves a good compromise between the complexity and the performance.

To improve the representation of spectral features and enhance the complementarity of heterogeneous spatial features in the joint classification of hyperspectral image (HSI) and light detection and ranging image (LiDAR), this paper proposes a joint classification network with interactive extraction and heterogeneous balance (JCNIH). On the one hand, this paper designs an interactive method for information exchange between large-span spectral features and small-span spectral features, which improves the adaptability and representation of spectral features. On the other hand, this paper designs a balance loss to constrain the extraction of heterogenous spatial features, which can keep the feature extraction balance and enhance the spatial complementarity. Then, the extracted spectral features and heterogeneous spatial features are aggregated for classification. Finally, comparative and ablation experiments demonstrate the effectiveness of JCNIH, and the experimental results show that the JCNIH outperforms the state-of-the-art methods in overall accuracy by 10.33% on three public datasets.

Image segmentation is an important task in computer vision and plays a critical role in many fields. Fuzzy Active Contour Model (FACM) has been widely applied in image segmentation because it can handle complex shape changes. However, it is difficult for current FACMs to obtain ideal performance when segmenting noisy images. Therefore, this paper proposes a Total Variation Diffusion-Guided Fuzzy Active Contour Model (TVDGFACM), which formulates noisy image segmentation as a hierarchical fusion process. Specifically, this model introduces total variation and adaptively fuses anisotropic and isotropic diffusion mechanisms to suppress noise interference while preserving image edges. Moreover, TVDGFACM fuses local intensity evidence according to regional dependencies and noise-aware pixel reliability, which helps adjust pixel memberships to generate a smooth and coherent segmentation result. Furthermore, a balance factor is constructed based on noise density, which is used to fuse global and local intensity information, thereby ensuring segmentation efficiency and stability. Experiments on synthetic, natural, remote sensing images, and large-scale benchmark dataset show that TVDGFACM is rational and effective.