Moving ship detection is vital for real-time maritime monitoring. Nevertheless, several challenges arise in this area: (i) Wide-area images often need to be sliced into patches to detect tiny targets, which is inefficient. (ii) The ships are small with almost no texture, leading to difficulties in accurate detection. (iii) The contrast between ships and ocean is relatively low, resulting in weak features. Although moving ships exhibit weak features, they often possess distinct wake trails. Capitalizing on this characteristic, we tailored a dual-head supervision network for moving ship detection. Initially, a dual-head supervision architecture is introduced to guide the model in using wake trails for target localization, thereby addressing the inefficiency caused by slicing. Subsequently, the background association head and target confirmation head are introduced to collaboratively enhance detection accuracy by leveraging inter-head attention mechanism. Finally, to address the issues of weak features, the dynamic feature enhancement module is embedded into backbone to boost the model’s feature extraction capability for moving targets. Experiments on GaoFen-1 dataset demonstrated that our method significantly improved the efficiency and performance of infrared moving ship detection and reached the state-of-the-art performance. Source codes will be available at https://github.com/KTqizhi/KTqizhi.github.io.

Due to recent advancements in deep learning, techniques for urban structure extraction and semantic segmentation of multimodal remote sensing images have significant improvements. However, the challenge arises from the variable color intensity and complex texture of urban structures in optical images, particularly in buildings and roads. Fortunately, the light detection and ranging (LiDAR) images promote the task of developing an optimal multimodal fusion network that effectively leverages information from different modalities. In this article, we propose an attention multiscale network (AMSNet) for binary semantic segmentation tasks focused on building extraction, as well as multiclass semantic segmentation tasks, by integrating optical and LiDAR remote sensing images. AMSNet introduces two feature fusion modules—spatial scale adaptive fusion (S2AF) and semantic guided fusion (SGF). S2AF facilitates feature fusion between optical and LiDAR images within the same layer. This module contains a spatial scale selection strategy and an adaptive weight learning strategy, which enables the network to adaptively extract and intentionally select multiscale features from multimodal data. SGF addresses the semantic gap between different layered block features through semantic feature guidance strategy while achieving feature fusion. Furthermore, we introduce robust feature learning (RFL) to ensure the network robustness in rotation and variation in objects, making it resilient to images captured from different viewpoints and sensors. RFL incorporates point-to-point similarity learning strategy and multiscale feature reuse strategy. Experimental results on publicly available datasets demonstrate that AMSNet outperforms other state-of-the-art models. Extensive ablation studies further confirm the significance of all key components in the proposed approach. The source code of this method is available athttps://github.com/B-LG-J/AMSNet.git.

Accurate and efficient ship tracking by geosynchronous orbit (GEO) satellites holds great significance for large-scale maritime surveillance. Nevertheless, ship tracking continues to grapple with a multitude of challenges as follows: 1) the targets are small and often obscured by cloud interference, leading to weakened features; 2) the contrasts between the ships and the background are relatively low, complicating the identification and tracking process; and 3) the frame-to-frame relative positioning accuracy is poor, posing difficulties in reflecting the actual movement trends of ships. In response to these challenges, we proposed TS-Track, a novel framework employing multilevel supervision paradigm to improve tracking performance. Initially, this framework restructured the tracking task into three key sub-modules: image enhancement, object tracking, and trajectory adjustment, inherently fostering a unified training protocol that naturally encompasses all components. Subsequently, a trajectory-based frame fusion strategy was proposed, utilizing consecutive three-frame images to enhance target features and produce consistent motion feature patterns; Last but not least, a trajectory adjustment network was developed to correct the position of ships during tracking, resulting in stable tracking trajectories, and reproduce the actual movement trends of ships. The experimental results on GaoFen-4 dataset validated that our method delivered a significant improvement in ship tracking and achieved state-of-the-art (SOTA) performance. Source codes are available athttps://github.com/KTqizhi/KTqizhi.github.io.

Infrared remote sensing images are becoming increasingly popular due to their superior penetration and resistance to light interference. However, challenges still remain when applying them in real-world applications: 1) raw infrared images suffer from severe stripes interference, and the preprocessing techniques used to obtain standard image products for subsequent detection tasks tend to be time-consuming, which fails to meet the application requirements; 2) current destriping techniques may inevitably weaken the local contrast between some objects and the local background since they need to consider the gray consistency of the overall image; 3) in low-resolution images, dim and small infrared targets are challenging to discriminate, resulting in high false alarms. To address these challenges, we proposed a progressive task-based universal network for raw infrared image ship detection while simultaneously removing stripes. First, we built an integrated network consisting of two components: the stripe denoising component (SDC) and the object detection component (ODC). We also designed a feedback loss adjustment mechanism to enhance the focus of the SDC on the target area. Second, a directed two-branch network was constructed for efficient stripe noise removal, including anx-direction branch for feature enhancement and ay-direction branch for grayscale smoothing. Finally, a parallel network with two labels was designed to extract the inherent features of the target and the background, as well as their relationship features, to achieve refined ship detection. We conducted experiments on a self-assembled dataset from the GaoFen-1 satellite to validate our approach. The experimental results demonstrated that the proposed method outperformed other state-of-the-art methods in infrared image ship detection.

Ship detection and tracking from remote sensing image sequences has become an increasingly important research point. However, there are still many challenges for ship tracking from low-resolution remote sensing image sequences: 1) the dim and small objects contain only a few shape and texture features, making it difficult to detect and track ships; 2) broken clouds often resemble ships, resulting in false tracking; 3) the ship may be occluded by clouds leading to missed tracking. To address these challenges, we proposed a novel multi-level supervision network for ship tracking from low-resolution remote sensing image sequences. First, we designed a gradient difference-guided object clarification network component to significantly improve the object saliency, which is also implemented based on the multi-frame correlation enhancement images to improve the feature strength of small targets in the input data. Second, to reduce the difficulty of completing complex tasks, a multi-level supervised network framework with multiple components was presented to achieve improving the target clarity, detecting targets and tracking targets step-by-step. Finally, to improve the trajectory integrity and tracking accuracy, a joint tracking method based on a low frame rate tracking criterion was proposed to control the state of target tracking module. The method was validated on a self-assembled dataset from the GaoFen-4 satellite. The experiment results show the stronger competitive and accuracy of the proposed method than other state-of-the-art object tracker.

Low-resolution ship detection from optical satellite image sequences is critical in high-orbit remote sensing satellite applications. However, it is still a difficult problem due to the following challenges: 1) the size of the ship is tiny in the low-resolution image; 2) the ship target is dim and the contrast with the background is low; 3) the interference of cloud and fog covering is complex and changeable. For these reasons, the targets are easily lost during the detection. In fact, the Clearer the Objects against to the background, the more Confident the Observers can detect it. In light of these considerations, we propose a COCO-Net to detect the small dynamic objects on low-resolution images in this paper. First, the multi-frame images are associated by introducing motion information as an effective compensation for small object features. Second, an integrated dual-supervised network that processes single-level tasks hierarchically is presented to adaptively enhance the input data quality of object detection without being limited by diverse scene disturbances. Third, a unified ROI-loss scheme that modulates the loss function of the first component by introducing ROI-masks from the second component is utilized to make the first component also work for object detection. In addition, we construct a new dataset for the small dynamic object detection based on the GaoFen-4 satellite imagery. Comprehensive experiments on a self-assembled dataset from the GaoFen-4 satellite show the superior performance of the proposed method compared to state-of-the-art object detectors.

It is observed that the great spectral variation in the same hyperspectral image (HSI) pixel class often leads to misclassification. To solve this problem, we have proposed an automatic clustering-based two-branch convolutional neural network (CNN): first, to reduce the intraclass spectral variation, the HSI pixels are automatically subdivided into smaller classes by clustering; second, in order to suppress the interference of spectral amplitude variation, the SincNet is introduced to capture the spectral pattern by giving more weight to the spectral shape; third, the DS-CNN with double directional strip convolution kernel is designed to extract spatial feature, so that specific contextual interactional features can be collected, especially in strip-shaped field-like roads and farmlands; finally, the spectral and spatial features extracted by the two branches are fused at fully connected layer to obtain an accurate classification. Extensive experiments demonstrated that the proposed method can obtain better classification performance than the state-of-the-art methods.