Infrared-visible image fusion and object detection are crucial components in remote sensing applications, each offering unique advantages. Recent research has increasingly sought to combine these tasks to enhance object detection performance. However, the integration of these tasks presents several challenges, primarily due to two overlooked issues: (i) existing infrared-visible image fusion methods often fail to adequately focus on fine-grained or dense information, and (ii) while joint optimization methods can improve fusion quality and downstream task performance, their multi-stage training processes often reduce efficiency and limit the network’s global optimization capability. To address these challenges, we propose the UniFusOD method, an efficient end-to-end framework that simultaneously optimizes both infrared-visible image fusion and object detection tasks. The method integrates Fine-Grained Region Attention (FRA) for region-specific attention operations at different granularities, enhancing the model’s ability to capture complex information. Furthermore, UnityGrad is introduced to balance the gradient conflicts between fusion and detection tasks, stabilizing the optimization process. Extensive experiments demonstrate the superiority and robustness of our approach. Not only does UniFusOD achieve excellent results in image fusion, but it also provides significant improvements in object detection performance. The method exhibits remarkable robustness across various tasks, achieving a 0.8 and 1.9 mAP₅₀ improvement over state-of-the-art methods on the DroneVehicle dataset for rotated object detection and the M3FD dataset for horizontal object detection, respectively.

In recent years, convolutional neural networks (CNNs) have made significant progress in remote sensing scene classification (RSSC) tasks. Because obtaining a large number of labeled images is time-consuming and expensive and the generalization ability of supervised models is limited, domain adaptation is widely introduced into RSSC. However, existing adaptation approaches mainly aim to align the distribution of features in a single representation space, which results in losing information and limiting the spatial range for extracting domain-invariant features. In addition, some of the methods simultaneously align pixel-level (local) and image-level (global) features for better results but suffer from searching for the best weight of the two parts manually, which is time-consuming and computing-expensive. To overcome the above issues, a novel feature fusion-and-alignment approach named Multi-Representation Dynamic Adaption Network (MRDAN) is proposed for cross-domain RSSC. Concretely, a Feature-Fusion Adaptation (FFA) module is embedded into the network, which maps samples to multiple representations and fuses them to obtain a broader domain-invariant feature space. Based on this hybrid space, we introduce a cross-domain Dynamic Feature-Alignment Mechanism (DFAM) to quantitatively evaluate and adjust the relative importance of the local and global adaptation losses during domain adaptation. The experimental results on the 12 transfer tasks between the UC Merced land-use, WHU-RS19, AID, and RSSCN7 data sets demonstrate the effectiveness of the proposed MRDAN over the state-of-the-art domain adaptation methods in RSSC.

In the practical application of remote sensing scene classification (RSSC), domain adaptation is introduced to handle the situation where the distribution of training data (source) and test data (target) is different. Compared to general domain adaptation, open set domain adaptation (OSDA) is suitable for more realistic situations where there are additional unknown classes in the target domain. The key to solving this problem is to separate unknown samples from target data to avoid negative transfer caused by mismatching unknown/known samples. In this letter, we propose a novel and effective method, named Instance Affinity metric-based Fine-grained Adaptation Network (IAFAN), for OSDA in RSSC. Concretely, an Unknown Sample Separation (USS) mechanism based on the instance affinity-aware matrix is pioneeringly proposed to endow the model with the ability to distinguish unknown samples. In addition, considering the high inter-class similarity and rich intra-class diversity of remote sensing images, we introduce the Sample Discriminability Enhancement (SDE) loss to further increase the inter-class distance and narrow the intra-class difference, thereby alleviating the negative transfer caused by sample misclassification and mismatching. In the feature confusion stage, we specially design Mask-mmd for OSDA as an adaptation metric to conduct semantic fine-grained cross-domain alignment of known samples while keeping unknown samples out of alignment, which avoids negative transfer during the adaptation process. Finally, we evaluate IAFAN on transfer tasks between different public remote sensing datasets, and the results verify that our method significantly outperforms previous methods in RSSC.

Despite significant advancements in remote sensing object tracking (RSOT) in recent years, achieving accurate and continuous tracking of tiny-sized targets remains a challenging task due to similar object interference and other related issues. In this paper, from the perspective of feature enhancement and a better feature matching strategy, we present a tracker SiamTM specifically designed for RSOT, which is mainly based on a new target information enhancement (TIE) module and a multi-level matching strategy. First, we propose a TIE module to address the challenge of tiny object sizes in satellite videos. The proposed TIE module goes along two spatial directions to capture orientation and position-aware information, respectively, while capturing inter-channel information at the global 2D image level. The TIE module enables the network to extract discriminative features of the targets more effectively from satellite images. Furthermore, we introduce a multi-level matching (MM) module that is better suited for satellite video targets. The MM module firstly embeds the target feature map after ROI Align into each position of the search region feature map to obtain a preliminary response map. Subsequently, the preliminary response map and the template region feature map are subjected to the Depth-wise Cross Correlation operation to get a more refined response map. Through this coarse-to-fine approach, the tracker obtains a response map with a more accurate position, which lays a good foundation for the prediction operation of the subsequent sub-networks. We conducted extensive experiments on two large satellite video single-object tracking datasets: SatSOT and SV248S. Without bells and whistles, the proposed tracker SiamTM achieved competitive results on both datasets while running at real-time speed.

The performance of deep learning approaches for Synthetic Aperture Radar (SAR) target detection is often limited by the scarcity of annotated data. While Self-Supervised Learning (SSL) has emerged as a powerful paradigm to mitigate data dependence, its potential in SAR target detection remains largely underexplored. In this study, we propose SARDet-MIM, a comprehensive framework based on Masked Image Modeling (MIM), to enhance SAR target detection. The approach consists of two stages. In the self-supervised pre-training stage, we propose an innovative Structural and Scattering Masked Autoencoder (SSMAE) method for SAR imagery. Unlike conventional MIM methods, which typically reconstruct raw pixels, SSMAE employs a physics-aware reconstruction target comprising multi-scale gradient and SAR-Harris features. This strategy explicitly guides the network to capture discriminative structural contexts and intrinsic scattering features that benefit SAR target detection. For downstream detection, we construct a Maximally Pre-trained Detector (MPD), which integrally transfers the pre-trained ViT encoder–decoder architecture to the detection network to fully exploit pre-trained representations. Extensive experiments on three SAR target detection datasets demonstrate that SARDet-MIM consistently outperforms competing methods.

Humans are capable of identifying aircraft based on quantitative features such as aspect ratio, engine count, wingspan, and structural configuration. Inspired by this, a keypoint-based aircraft identification approach is proposed to address the challenge of fine-grained aircraft recognition in high-resolution remote sensing images. First, a dataset of aircraft labeled with keypoints is built, in which aircraft are reclassified into types according to the similarity of keypoint distributions to improve extraction stability and versatility. Then, a keypoint extraction method with topological constraints is proposed, leveraging the nadir imaging characteristics of remote sensing and accounting for the relationships among keypoints. Subsequently, distinctive quantitative features for identification are selected through representativeness and effectiveness analyses for the following matching algorithm. Finally, a comprehensive template matching-based identification strategy is proposed to recognize targets based on quantitative descriptions derived from keypoints. This novel solution achieves significantly more accurate identification than traditional regression–classification approaches, improving recognition accuracy by over $3\%$ on average. Moreover, the method extends aircraft identification capabilities from closed-set to open-set recognition, demonstrating substantial value for the precise interpretation of aircraft targets in high-resolution optical imagery.