With the rapid growth of the marine economy and the increasing demand for maritime security, ship target detection has become critically important in both military and civilian applications. However, in complex remote sensing scenarios, challenges such as visual similarity among ships, subtle inter-class differences, and the continual emergence of new categories make traditional closed-world detection methods inadequate. To address these issues, this paper proposes an open-world detection framework for remote sensing ships. The framework integrates two key modules: (1) a Fine-Grained Feature and Extreme Value-based Unknown Recognition (FEUR) module, which leverages tail distribution modeling and adaptive thresholding to achieve precise detection and effective differentiation of unknown ship targets; and (2) a Joint Optimization-based Incremental Learning (JOIL) module, which employs hierarchical elastic weight constraints to differentially update the backbone and detection head, thereby alleviating catastrophic forgetting while incorporating new categories with only a few labeled samples. Extensive experiments on the FGSRCS dataset demonstrate that the proposed method not only maintains high accuracy on known categories but also significantly outperforms mainstream open-world detection approaches in unknown recognition and incremental learning. This work provides both theoretical value and practical potential for continuous ship detection and recognition in complex open environments.

In the field of remote sensing, detection in dimly lit or shadowed areas has traditionally been difficult because of detector noise. Given that noise in real-world images of remote sensing exhibits spatial correlation, existing self-supervised methods encounter difficulties in reconciling the suppression of spatially correlated noise with the preservation of local texture details. To address this challenge, we propose a self-supervised model that combines blind spot feature extraction with diffusion-based texture generation to fine denoising of real-world images under adverse conditions. We first introduce a blind spot feature extraction structure based on the fusion of U-Net with Blind Spot Net (UBSN) and Blind Transformer (BTF). In UBSN, we integrate multi-stride Blind Spot Convolution(BSC) + Dilated Convolution (DC) feature extraction nodes and employ a Reshuffle strategy in skip-layer connections to maintain large-scale blind spot characteristics. Additionally, we design a transformer structure for blind spot between patches to remove the noise with spatial correlations while ensuring global feature acquisition. Subsequently, to restore texture details blurred by the blind spot structures, we introduce a texture generation diffusion structure during model training, achieving a balance between large-scale blind spot characteristics and local rich texture details. Experimental results demonstrate that our approach outperforms other self-supervised denoising methods, even some methods leveraging unpaired images, without the need for parameters related to on-orbit satellite detectors.

Image stitching aims to align two images from different perspectives. For infrared remote sensing images, the low resolution, lack of strong feature points, and the presence of large texture-less regions make it difficult to achieve effective feature matching and high-quality image stitching results. In the field of remote sensing stitching, the primary challenge is how to effectively extract features, reduce the influence of parallax, and improve registration accuracy. To improve the image stitching performance and obtain parallax-tolerant fine registration results, we propose a two-stage image stitching method based on unsupervised learning. Firstly, in the first stage, we use a multi-level feature extraction network to effectively extract image correlation features, progressively refining the registration from coarse to fine, thus ensuring performance under large-baseline conditions. Secondly, by utilizing a discrete feature detection module in the multi-level network, we remove anomalous feature regions and recombine effective local feature regions, enabling the fusion of detailed features with global features and improving registration accuracy. Finally, in the second stage, an image fine adjustment module is applied to process the image background and foreground, further eliminating parallax artifacts and improving registration accuracy. Compared with existing methods, our method has advantages in both registration accuracy and parallax tolerance. Extensive experiments demonstrate that our method effectively registers and stitches infrared remote sensing images on both the self-built infrared remote sensing dataset and the publicly available UDIS-D dataset, outperforming current state-of-the-art (SoTA) methods in terms of performance. The code is available at https://github.com/Shamfeeding/Image-Stitching.

Conventional imaging systems often suffer from a coupled limitation of narrow field of view, single modality, and insufficient effective resolution. Wide-angle imaging preserves scene context but compresses distant or small-scale targets into limited pixels, while narrow-field imaging improves details at the cost of global perception. Moreover, single-modal visible imaging is sensitive to illumination and contrast variations, whereas infrared imaging lacks fine spatial texture. To increase information at the imaging source, we propose coupled spectral–spatial fusion-enabled multi-scale panoramic imaging, a dual-field-of-view (FOV) visible–infrared framework for wide-field high-resolution perception. Two imaging units acquire paired visible and infrared images from adjacent overlapping views. For each view, a visible–infrared fusion super-resolution model integrates visible structural details with infrared radiative cues to reconstruct a high-resolution fused image. A multi-scale stitching algorithm then extracts robust features, estimates cross-view correspondences, and merges the two fused images into a large-FOV panoramic result. Outdoor experiments demonstrate that the proposed method improves local contrast, suppresses pixelation artifacts, enhances readable fine details, and expands the observable field of view, providing an effective route toward multimodal panoramic imaging.