Evidence theory, also called belief functions theory, provides an efficient tool to represent and combine uncertain information for pattern classification. Evidence combination can be interpreted, in some applications, as classifier fusion. The sources of evidence corresponding to multiple classifiers usually exhibit different classification qualities, and they are often discounted using different weights before combination. In order to achieve the best possible fusion performance, a new Credal Belief Redistribution (CBR) method is proposed to revise such evidence. The rationale of CBR consists in transferring belief from one class not just to other classes but also to the associated disjunctions of classes (i.e., meta-classes). As classification accuracy for different objects in a given classifier can also vary, the evidence is revised according to prior knowledge mined from its training neighbors. If the selected neighbors are relatively close to the evidence, a large amount of belief will be discounted for redistribution. Otherwise, only a small fraction of belief will enter the redistribution procedure. An imprecision matrix estimated based on these neighbors is employed to specifically redistribute the discounted beliefs. This matrix expresses the likelihood of misclassification (i.e., the probability of a test pattern belonging to a class different from the one assigned to it by the classifier). In CBR, the discounted beliefs are divided into two parts. One part is transferred between singleton classes, whereas the other is cautiously committed to the associated meta-classes. By doing this, one can efficiently reduce the chance of misclassification by modeling partial imprecision. The multiple revised pieces of evidence are finally combined by Dempster-Shafer rule to reduce uncertainty and further improve classification accuracy. The effectiveness of CBR is extensively validated on several real datasets from the UCI repository, and critically compared with that of other related fusion methods.

Change detection in heterogeneous remote sensing images is crucial for disaster damage assessment. Recent methods use homogenous transformation, which transforms the heterogeneous optical and SAR remote sensing images into the same feature space, to achieve change detection. Such transformations mainly operate on the low-level feature space and may corrupt the semantic content, deteriorating the performance of change detection. To solve this problem, this paper presents a new homogeneous transformation model termed deep homogeneous feature fusion (DHFF) based on image style transfer (IST). Unlike the existing methods, the DHFF method segregates the semantic content and the style features in the heterogeneous images to perform homogeneous transformation. The separation of the semantic content and the style in homogeneous transformation prevents the corruption of image semantic content, especially in the regions of change. In this way, the detection performance is improved with accurate homogeneous transformation. Furthermore, we present a new iterative IST (IIST) strategy, where the cost function in each IST iteration measures and thus maximizes the feature homogeneity in additional new feature subspaces for change detection. After that, change detection is accomplished accurately on the original and the transformed images that are in the same feature space. Real remote sensing images acquired by SAR and optical satellites are utilized to evaluate the performance of the proposed method. The experiments demonstrate that the proposed DHFF method achieves significant improvement for change detection in heterogeneous optical and SAR remote sensing images, in terms of both accuracy rate and Kappa index.

Convolutional neural networks (CNNs) have been widely applied in the context of ship detection in synthetic aperture radar (SAR) images, but the detection performance is still not ideal in scenarios with clutter interference. Mining frequency-domain information to suppress the sea clutter in SAR ship detection has attracted wide attention. However, existing frequency-domain ship detection methods do not process frequency-domain information adaptively, which results in the degradation of ship detection performance. To overcome this problem, this article proposes a novel deep learning network called YOLO-FA. YOLO-FA contains the proposed Frequency Attention Module (FAM), which can process frequency-domain information of SAR images adaptively. The proposed method can suppress the sea clutter in the SAR images with the help of frequency-domain information. We evaluate the proposed method YOLO-FA on two datasets, i.e., the High-Resolution SAR images dataset (HRSID) and SAR Ship Detection Dataset (SSDD). Compared with the baseline method YOLOv5 and existing commonly used methods, YOLO-FA achieves state-of-the-art detection performance on both datasets.

Infrared Few-Shot Object Detection (IFSOD) aims to detect infrared objects with limited labeled examples. Current infrared datasets, however, suffer from limited diversity in object types and classes, hindering robust evaluation of model generalization on novel classes. To systematically assess dataset quality, we propose metrics for class diversity, instance variability, and object density. By integrating three widely used infrared datasets, we construct the first dataset specifically tailored for IFSOD, increasing instance density to 4.8 (a 1.1 improvement) and expanding the number of classes to 18 (a 5-class increase) compared to the source datasets. Furthermore, frequency analysis of spatial feature reveals that sparse annotations introduce spectral bias in frequency domain. Directly transforming spatial features to the frequency domain, however, mixes background noise with object features, causing spectral leakage and impairing the learning of discriminative features for novel classes. To address these issues, we propose the Frequency Compression Few-Shot Detection (FC-fsd) method, which incorporates a Frequency Compression (FC) module. The FC module leverages Discrete Cosine Transform (DCT) within localized windows to reduce spectral leakage and enhance feature clarity. With minimal additional computational overhead, FC-fsd significantly outperforms state-of-the-art methods, achieving nAP50 scores of 28.57 (+13.37) and 35.63 (+2.59) in 1-shot and 2-shot settings, respectively. Our dataset is published at https://github.com/RuihengZhang/IFSOD-dataset.

Image matching is a primary technology for optical and synthetic aperture radar (SAR) image fusion but often shows limited performance due to the highly nonlinear differences between optical and SAR modalities. Recently, deep neural networks (DNNs) have been investigated to effectively extract nonlinear features for image matching tasks, where DNNs are trained based on the elaborated design of loss functions and a low loss value is often expected to obtain better image matching performance. In this letter, we first theoretically demonstrate that when the value of a state-of-the-art loss function decreases, the corresponding matching performance may not consistently improve due to the imbalanced effect of positive and negative samples. To tackle this issue, we proposed an improved loss function to train DNNs for image matching of SAR and optical images. We theoretically prove that the improved loss function ensures the improvement of the matching performance when the loss value decreases based on Taylor’s series expansion analysis. Experimental results on an open dataset with extensive optical and SAR image pairs show that 1) the proposed loss function is better than the original one in terms of image matching performance and 2) the combination of our loss function and existing multiscale convolutional gradient feature (MCGF)-based network provides better matching performance than other state-of-art approaches.

Space-borne synthetic aperture radar (SAR) and optical sensors are important tools for building damage detection. Fusion of SAR and optical images improves detection performance. However, when the resolutions of the two different kinds of images differ, the performance of the existing pixel-level fusion methods deteriorates significantly due to interpolation-induced distortion. To solve this problem, this paper presents a new superpixel-based belief fusion (SBBF) model for building damage detection. The superpixels on the SAR and optical images are identified by the segmentation on the pre-earthquake optical image to perform the fusion on the superpixel-level instead of the pixel-level in existing methods. Then in the fusion stage, different from the commonly used direct fusion methods that do not consider the reliability in the fusion process, a novel belief fusion method that employs a basic belief assignment (BBA) to incorporate different reliabilities of superpixels is proposed to improve the accuracy of building damage detection. For each superpixel, the BBA is assigned based on the influence of noise and resolutions. The United Nations Operational Satellite Applications Programme (UNOSAT) datasets corresponding to the 2010 Haiti earthquake and the 2011 Tohoku earthquake, are used to evaluate the performance of the proposed method. The experimental results show that the proposed method achieves significantly better performance than existing separate SAR or optical images based methods, and the existing pixel-level fusion methods.