Pan-sharpening is a commonly employed strategy to obtain high-resolution multispectral (HRMS) images. Existing deep unfolding networks for pan-sharpening suffer from ineffectively establishing the relationship between panchromatic (PAN) images and generated noisy HRMS (GN-HRMS) images in PAN-guided image denoising, lacking the support of physical models. In this paper, we first design a degradation-fusion-aware unfolding framework (DF-UF) to separate the processing of PAN-prior in PAN-guided image denoising into an individual module, PAN-prior processor, for better integrating physical models. Then, we derive a flexible intensity-hue-saturation (F-IHS) to act as the PAN-prior processor, which models the relationship between PAN images and GN-HRMS images in terms of intensity components through the intensity-hue-saturation (IHS) theory. Finally, plugging F-IHS into DF-UF, we propose a degradation-intensity-aware unfolding transformer (DIUT) to address the problem of incomplete utilization of PAN images in the denoising process. Extensive experiments on diverse scenes show that the performance of DIUT surpasses existing state-of-the-art methods.

This paper proposes a moment-constrained marginal distributionally robust Kalman filter (MC-MDRKF) for centralized state estimation in multi-sensor systems with unknown sensor noise correlations. We first derive a robust static estimator and then extend it to dynamic systems for the MC-MDRKF algorithm. The static estimator defines a marginal distributional uncertainty set using moment constraints and formulates a minimax optimization problem to robustly address unknown correlations. We prove that this minimax problem admits an equivalent convex optimization formulation, enabling efficient numerical solutions. The resulting MC-MDRKF algorithm recursively updates state estimates in dynamic state-space models. Simulation results demonstrate the superiority and robustness of the proposed method in a multi-sensor target tracking scenario.

The fusion module of RGB and infrared (IR) remote sensing images is the key of multispectral ship detection. Existing works have shown that the cross-attention-based feature fusion can achieve good performance by extracting the complementary information of RGB and IR modalities. However, the existing commonly used cross-attention mechanisms introduce lots of redundancy parameters and mainly focus on global feature interaction of multispectral images, ignoring local detail information that is also important for small ship detection. In this paper, we propose a novel multispectral ship detection approach named LoGFusion. In LoGFusion, we design the cross stage partial module with partial convolution (CSPMPC) to reduce feature redundancy and utilize the local cross-modal fusion module (LoCFM) and global cross-modal fusion module (GCFM) to capture both local and global cross-modal features. Furthermore, we introduce a Multispectral Small Ship Dataset (MSSD) containing over 5k ship targets for small target detection. Experiments on MSSD validate the effectiveness of our method in terms of small ship detection in multispectral images.

As the number of remote sensing (RS) images increases, it is crucial to retrieval ship targets according to specific demands. The existing ship image retrieval methods only extract features from the image modality, which may not fully utilize the rich text information available and ignore the high-level hierarchical relations between ship classes. In this paper, we propose a language-assisted siamese contrastive framework, namely LASCF, for fine-grained ship retrieval in RS images. In the new LASCF, the siamese vision models are employed to measure the similarity between images. Moreover, a label text encoder with a pretrained language model is designed to extract the high-level semantic information from labels, and thus the information of the hierarchical relations between ship classes are fused in LASCF. Finally, the multimodal similarity measurement module based on contrastive learning is proposed to optimize the siamese vision models. The experimental results show that the proposed LASCF outperforms several existing state-of-the-art methods.

Deep networks require a large number of samples for optimization, so few-shot segmentation in remote sensing scenes is still an open problem. However, this challenge is exacerbated by the feature blurring and aliasing of bodies (low frequency) and boundaries (high frequency). The existing methods usually only focus on the body part of the class, that is, the low-frequency part, and ignore the critical role of boundary information, that is, high-frequency details, on feature representation. In this letter, we propose a novel body joint boundary prototype match (B2PM) approach that aims to enable prior learning of low- and high-frequency information by explicitly modeling the body and boundary features of objects. First, body-aware prototype learning (BodyPL) realizes the adaptive modeling of the body part of the object through a precise farthest point sampling (FPS) initialization algorithm and an adaptive part shift (APS) strategy, which alleviates the feature ambiguity of the body. Second, boundary-aware prototype learning (BoundPL) explicitly models boundary prototypes by building a patch division and assignment strategy to alleviate feature aliasing at boundaries. Finally, prototype match performs prior knowledge aggregation by computing the affinity between query features and support prototypes. Extensive experiments on commonly used benchmarks (iSAID and PASCAL VOC) demonstrate that B2PM improves the state of the art by significant margins.

In high-resolution automotive radar tracking systems, vehicle targets are often regarded as extended targets, which means multiple measurements originated from scattering centers of vehicle targets can be detected at each scan and thus the traditional point target tracking schemes are unsuitable. Meanwhile, vehicle maneuvers, e.g., braking and swerving, cause serious degradation of the classical extended target tracking methods. In this paper, a novel method is proposed for maneuvering extended vehicle tracking with automotive radar. The data-region association (DRA) strategy is adopted to handle the vehicle extension effect, which is superior in describing the complex spatial distribution of vehicle target measurements. The interacting multiple model (IMM) method is combined with this DRA strategy to describe the evolution of target motion models. Accordingly, the proposed DRA-IMM method achieves satisfying tracking performance of extended vehicles and also guarantees the robustness in case of maneuvers. Furthermore, in view of the correlation between vehicle extension and its kinematic state, a ray-based strategy is devised to improve the prior distribution of the data-region association of the basic DRA-IMM, and accordingly an enhanced DRA-IMM (EDRA-IMM) method is proposed. Simulation result validates the effectiveness of the proposed DRA-IMM method for maneuvering extended vehicle tracking and the further improvement of the proposed EDRA-IMM method.

Model uncertainty is a serious challenge for robustness of tracking algorithms in radar systems. The smooth variable structure filter (SVSF) achieves error-bounded estimations for target state by scaling the magnitude of kinematic modeling error and accordingly performing a flexible switching strategy for the correction gain. However, the SVSF, without any smoothing functions, suffers from undesired chattering phenomenon since the measurement noise causes random disturbance to the identification of actual level of uncertainties, leading to obvious deterioration of tracking accuracy. In this paper, we present a new switching function for SVSF, i.e. the hyperbolic tangent function, for effective chattering suppression. Then we propose a new algorithm named as the Tanh-SVSF, which reformulates the correction gain with the new switching function, to improve the estimation accuracy for target state. A mathematical definition of SVSF chattering is proposed to quantify the chattering amplitude. It is demonstrated that the new switching function exerts a nonlinear compressing effect on the likelihood of measurement innovation and substantially reduces the disturbance of measurement noise, leading to elimination of the chattering problem. The stability of the Tanh-SVSF is analyzed, based on a proposed stability theorem and the numerical exhaustion strategy. Finally, the proposed method is tested on a simulated vehicle tracking scenario and real-world radar data from the Oxford Radar RobotCar Dataset, and shows superior performance over existing SVSF formulations and the Kalman filter, in view of tracking accuracy, track continuity and the proposed chattering indicator.

This paper concerns the robust state estimation of automotive radar targets in presence of model uncertainty. Smooth variable structure filter (SVSF) achieves error-bounded estimation for target state, even with an inaccurate description of target kinematic model. However, it suffers the undesired chattering phenomenon especially in case of a high model uncertainty level, and its performance is sensitive to a preset smoothing boundary layer parameter. In this paper, we propose a novel hybrid SVSF algorithm to handle these two problems simultaneously. First, we derive a nonlinear generalized variable smoothing boundary layer (NGVBL) parameter based on the conventional Tanh-SVSF method by minimizing the pseudo posterior estimation error covariance. Then this NGVBL is employed to realize an adaptive two-module switching strategy with respect to the uncertainty level to calculate the correction gain. If the uncertainty level is high, the undesired chattering is effectively suppressed by the standard Tanh-SVSF gain. In case of a low uncertainty level, the NGVBL is utilized to replace the preset smoothing boundary layer parameter and reformulate the correction gain. Furthermore, it is demonstrated that the NGVBL-based gain is quasi-optimal in the mean square error (MSE) sense. Accordingly, this novel NGVBL-based hybrid SVSF (NGVBL-SVSF) algorithm improves the estimation performance by avoiding parameter sensitivity in a low uncertainty level case, and maintains effective chattering suppression and robustness to increasing uncertainties. Simulation and real-world automotive radar data experiment results show that, the proposed NGVBL-SVSF outperforms existing SVSFs and the classical Kalman filter in terms of tracking accuracy and track continuity.