The detection and recognition of ships holds crucial significance across various domains, including transportation, fishing, smuggling prevention, and rescue operations. To achieve accurate and efficient ship detection and recognition, remote sensing technologies, specifically, optical and synthetic aperture radar (SAR) sensors, have been widely employed. However, each sensor modality presents its own limitations, such as cloud cover in optical images and interference in SAR images, resulting in restricted data availability and false alarms during detection. This article provides a comprehensive summary of recent advancements in the fusion of optical and SAR images for ship detection and recognition. Meanwhile, the fusion of remote sensing images and in situ data, such as automatic identification system (AIS) data, is briefly summarized, considering significant benefits and technical trends. This article highlights the immense potential of integrating these two sensor modalities to overcome individual challenges and enhance overall system performance. One of the primary challenges addressed in this work pertains to accurately matching ship targets between optical and SAR images when ships change positions. However, recently developed multimodal fusion networks and transfer learning techniques have the potential to tackle the challenges, even with unmatched optical and SAR image pairs. In addition to summarizing existing techniques, the article delves into future perspectives on fusion-based ship detection and recognition. In conclusion, this survey highlights the current state of optical and SAR image fusion techniques, pinpoints existing challenges, and outlines future research prospects, emphasizing the significance of fusion-based approaches in shaping the future of maritime applications.

Despite CNN-based deblur models have shown their superiority when solving motion blurs, restoring a photorealistic image from severe motion blur remains an ill-posed problem due to the loss of temporal information and textures. Event cameras such as Dynamic and Active-pixel Vision Sensor (DAVIS) can simultaneously produce gray-scale Active Pixel Sensor (APS) frames and events, which can capture fast motions as events of very high temporal resolution, i. e. , $1~\mu s$ , can provide extra information for blurry APS frames. Due to the natural noise and sparsity of events, we employ a recurrent encoder-decoder architecture to generate dense recurrent event representations, which encode the overall historical information. We concatenate the original blurry image with the event representation as our hybrid input, from which the network learns to restore the sharp output. We conduct extensive experiments on GoPro dataset and a real event blurry dataset captured by DAVIS240C. Our experimental results on both synthetic and real images demonstrate state-of-the-art performance for $1280\times 720 $ images at 30 fps.

The change detection aims to identify temporal changes in land cover. In emergency disaster scenarios, acquiring postchange optical images is often difficult due to factors such as adverse weather and illumination conditions. In contrast, the SAR-based change detection is robust to these environmental factors but is prone to speckle noise and often lacks clear semantic interpretation. These challenges highlight the importance of multimodal approaches that integrate the complementary information from different data sources. To address the domain gap between optical and SAR data, we propose change detection prompt (CDPrompt), an automatic prompt-learning framework that leverages in-domain change information as prompts to suppress fake changes caused by the domain gap between the two modalities. CDPrompt incorporates a modality-specific domain tuning module (DTM) to inject the domain knowledge into the segment anything model (SAM), enabling efficient adaptation to multimodal data with minimal labels and training costs. A low-level enhancement module (LwEM) further refines spatial details using historical optical images, while a consistency loss enhances the learning of domain-invariant features between prechange optical and SAR images. To support evaluation in disaster scenarios with missing modalities, we extend the DFC25 dataset and introduce the first disaster-oriented multimodal change detection dataset, DFC25-Extended, comprising DFC25-OS-S and DFC25-O-SO. Extensive experiments on the Onera Satellite Change Detection (OSCD) and DFC25-Extended datasets demonstrate the superior performance and practical value of CDPrompt. The code and dataset will be publicly available at: https://github.com/zhanglimeng13/CDPrompt

Deep neural networks (DNNs) have shown strong performance in synthetic aperture radar (SAR) image classification. However, their “black-box” nature limits interpretability and poses challenges for robustness, which is critical for sensitive applications such as disaster assessment, environmental monitoring, and agricultural insurance. This study systematically evaluates the adversarial robustness of five representative DNNs (VGG11/16, ResNet18/101, and A-ConvNet) under a variety of attack and defense settings. Using eXplainable AI (XAI) techniques and attribution-based visualizations, we analyze how adversarial perturbations and adversarial training affect model behavior and decision logic. Our results reveal significant robustness differences across architectures, highlight interpretability limitations, and suggest practical guidelines for building more robust SAR classification systems. We also discuss challenges associated with large-scale, multi-class land use and land cover (LULC) classification under adversarial conditions.

With the rapid growth of multimodal remote sensing (RS) data, there is an increasing demand for intelligent onboard computing to alleviate the transmission and latency bottlenecks of traditional orbit-to-ground downlinking workflows. While many lightweight AI algorithms have been widely developed and deployed for onboard inference, their limited generalization capability restricts performance under the diverse and dynamic conditions of advanced Earth observation. Recent advances in remote sensing foundation models (RSFMs) offer a promising solution by providing pretrained representations with strong adaptability across diverse tasks and modalities. However, the deployment of RSFMs onboard resource-constrained devices such as nano satellites remains a significant challenge due to strict limitations in memory, energy, computation, and radiation tolerance. To this end, this review proposes the first comprehensive survey of onboard RSFMs deployment, where a unified deployment pipeline including RSFMs development, model compression techniques, and hardware optimization is introduced and surveyed in detail. Available hardware platforms are also discussed and compared, based on which some typical case studies for low Earth orbit (LEO) CubeSats are presented to analyze the feasibility of onboard RSFMs’ deployment. To conclude, this review aims to serve as a practical roadmap for future research on the deployment of RSFMs on edge devices, bridging the gap between the large-scale RSFMs and the resource constraints of spaceborne platforms for onboard computing.