Accuracy Of Ship Detection Research Articles

Stepwise Attention-Guided Multiscale Fusion Network for Lightweight and High-Accurate SAR Ship Detection

Many exceptional deep learning networks have demonstrated remarkable proficiency in general object detection tasks. However, the challenge of detecting ships in synthetic aperture radar (SAR) imagery increases due to the complex and various nature of these scenes. Moreover, sophisticated large-scale models necessitate substantial computational resources and hardware expenses. To address these issues, a new framework is proposed called a stepwise attention-guided multiscale feature fusion network (SAFN). Specifically, we introduce a stepwise attention mechanism designed to selectively emphasize relevant information and filter out irrelevant details of objects in a step-by-step manner. Firstly, a novel LGA-FasterNet is proposed, which incorporates a lightweight backbone FasterNet with lightweight global attention (LGA) to realize expressive feature extraction while reducing the model’s parameters. To effectively mitigate the impact of scale and complex background variations, a deformable attention bidirectional fusion network (DA-BFNet) is proposed, which introduces a novel deformable location attention (DLA) block and a novel deformable recognition attention (DRA) block, strategically integrating through bidirectional connections to achieve enhanced features fusion. Finally, we have substantiated the robustness of the new framework through extensive testing on the publicly accessible SAR datasets, HRSID and SSDD. The experimental outcomes demonstrate the competitive performance of our approach, showing a significant enhancement in ship detection accuracy compared to some state-of-the-art methods.

YOLO-Ships: Lightweight ship object detection based on feature enhancement

Due to the influences of complex shore-based backgrounds, ship scale changes and occlusions, most deep learning-based ship target detection algorithms yield false and missed detections, and the number of required model parameters is large. Therefore, we present a lightweight and effective method called YOLO-Ships. An improved lightweight Ghost module was used to enhance the ability of the network to extract ship features. In addition, we designed a ship feature enhancement module and a lightweight C3REGhost module to strengthen the important feature information and improve the detection ability and localization accuracy of the model. Finally, the model loss function was redesigned to improve the convergence speed and accuracy of the model. Our experimental results showed that the mAP@0.5 increased by 5.2% and the number of parameters decreased by 2.89 M compared to those of YOLOv5s, effectively achieving a balance between model lightweightness and good ship detection accuracy.

SSMA-YOLO: A Lightweight YOLO Model with Enhanced Feature Extraction and Fusion Capabilities for Drone-Aerial Ship Image Detection

Due to the unique distance and angles involved in satellite remote sensing, ships appear with a small pixel area in images, leading to insufficient feature representation. This results in suboptimal performance in ship detection, including potential misses and false detections. Moreover, the complexity of backgrounds in remote sensing images of ships and the clustering of vessels also adversely affect the accuracy of ship detection. Therefore, this paper proposes an optimized model named SSMA-YOLO, based on YOLOv8n. First, this paper introduces a newly designed SSC2f structure that incorporates spatial and channel convolution (SCConv) and spatial group-wise enhancement (SGE) attention mechanisms. This design reduces spatial and channel redundancies within the neural network, enhancing detection accuracy while simultaneously reducing the model’s parameter count. Second, the newly designed MC2f structure employs the multidimensional collaborative attention (MCA) mechanism to efficiently model spatial and channel features, enhancing recognition efficiency in complex backgrounds. Additionally, the asymptotic feature pyramid network (AFPN) structure was designed for progressively fusing multi-level features from the backbone layers, overcoming challenges posed by multi-scale variations. Experiments of the ships dataset show that the proposed model achieved a 4.4% increase in mAP compared to the state-of-the-art single-stage target detection YOLOv8n model while also reducing the number of parameters by 23%.

Enhancing object detection and classification using deep learning: A novel approach with annotated dataset and YOLO-C3GTR architecture

In the rapidly evolving field of computer vision, the need for advancements in object detection is paramount. For a variety of reasons, including security, maritime surveillance, and environmental monitoring, the identification and categorization of ships in aerial pictures is an essential component. In this study, two distinctive approaches are proposed to improve the accuracy of ship detection and categorization in satellite imagery. In the beginning, the dataset for ship detection that was provided by Airbus was separated into five distinct categories: oil tanker, bulk carrier, container ship, sand carrier, and general ship class. Setting up this division was done with the idea of increasing the precision of the ship classification in the imagery. Moreover, we deployed the YOLO-C3GTR model, which is part of our Object Detection framework and helped us to distinguish ships using the technique of Object Detection. The application of the methodology that we have proposed has the potential to significantly improve the precision of these systems when they are used in real-world scenarios and to make a novel addition to the field of ship identification and classification. We have used multiple algorithms of YOLOv5 tested on this dataset like TPHYOLOv5, YOLOv5x, YOLOv5l and our proposed architecture YOLO-C3GTR and it surpassed all the existing approaches by the mAP of 0.83. Furthermore, we intend to perform additional research in this field while simultaneously working to improve our technique. A comparison was made between the performance of the model and that of other deep learning approaches. The model was evaluated and trained with the categorized dataset.

Closely arranged inshore ship detection using a bi-directional attention feature pyramid network

ABSTRACTThe detection of inshore ships in Synthetic Aperture Radar (SAR) images is seriously disturbed by shore buildings, especially for closely arranged inshore ships whose appearance is similar when compared with detection of deep-sea ships. There are many interference factors such as speckle noise, cross sidelobes, and defocusing in SAR images. These factors can seriously interfere with feature extraction, and the traditional Fully Convolutional One-Stage (FCOS) network often cannot effectively distinguish small-scale ships from backgrounds. Additionally, for closely arranged inshore ships, missed detections and inaccurate positioning often occur. In this paper, a method of inshore ship detection based on Bi-directional Attention Feature Pyramid Network (BAFPN) is proposed. In order to improve the detection ability of small-scale ships, the BAFPN is based on the FCOS network, which connects a Convolutional Block Attention Module (CBAM) to each feature map of the pyramid and can extract rich semantic features. Then, the idea from Path-Aggregation Network (PANet) is adopted to splice a bottom-up pyramid structure behind the original pyramid structure, further highlighting the features of different scales and improving the ability of the network to accurately locate ships under complex backgrounds, thereby avoiding missed detections in closely arranged inshore ship detection. Finally, a weighted feature fusion method is proposed, which makes the feature information extracted from the feature map have different focuses and can improve the accuracy of ship detection. Experiments on SAR image ship datasets show that the mAP for the SSDD and HRSID reached 0.902 and 0.839 respectively. The proposed method can effectively improve the ship positioning accuracy while maintaining a fast detection speed, and achieves better results for ship detection under complex background.

LRTransDet: A Real-Time SAR Ship-Detection Network with Lightweight ViT and Multi-Scale Feature Fusion

In recent years, significant strides have been made in the field of synthetic aperture radar (SAR) ship detection through the application of deep learning techniques. These advanced methods have substantially improved the accuracy of ship detection. Nonetheless, SAR images present distinct challenges, including complex backgrounds, small ship targets, and noise interference, thereby rendering the detectors particularly demanding. In this paper, we introduce LRTransDet, a real-time SAR ship detector. LRTransDet leverages a lightweight vision transformer (ViT) and a multi-scale feature fusion neck to address these challenges effectively. First, our model implements a lightweight backbone that combines convolutional neural networks (CNNs) and transformers, thus enabling it to simultaneously capture both local and global features from input SAR images. Moreover, we boost the model’s efficiency by incorporating the faster weighted feature fusion (Faster-WF2) module and coordinate attention (CA) mechanism within the feature fusion neck. These components optimize computational resources while maintaining the model’s performance. To overcome the challenge of detecting small ship targets in SAR images, we refine the original loss function and use the normalized Wasserstein distance (NWD) metric and the intersection over union (IoU) scheme. This combination improves the detector’s ability to efficiently detect small targets. To prove the performance of our proposed model, we conducted experiments on four challenging datasets (the SSDD, the SAR-Ship Dataset, the HRSID, and the LS-SSDD-v1.0). The results demonstrate that our model surpasses both general object detectors and state-of-the-art SAR ship detectors in terms of detection accuracy (97.8% on the SSDD and 93.9% on the HRSID) and speed (74.6 FPS on the SSDD and 75.8 FPS on the HRSID), all while demanding 3.07 M parameters. Additionally, we conducted a series of ablation experiments to illustrate the impact of the EfficientViT, the Faster-WF2 module, the CA mechanism, and the NWD metric on multi-scale feature fusion and detection performance.

A Novel Decoupled Feature Pyramid Networks for Multi-Target Ship Detection.

The efficiency and accuracy of ship detection is of great significance to ship safety, harbor management, and ocean surveillance in coastal harbors. The main limitations of current ship detection methods lie in the complexity of application scenarios, the difficulty in diverse scales object detection, and the low efficiency of network training. In order to solve these problems, a novel multi-target ship detection method based on a decoupled feature pyramid algorithm (DFPN) is proposed in this paper. First, a feature decoupling module is introduced to separate ship contour features and position features from the multi-scale fused features, to overcome the problem of similar features in multi-target ships. Second, a feature pyramid structure combined with a gating attention module is constructed to improve the feature resolution of small ships by enhancing contour features and spatial semantic information. Finally, a feature pyramid-based multi-feature fusion algorithm is proposed to improve the adaptability of the network to changes in ship scale according to the contextual relationship of ship features. Experiments on the multi-target ship detection dataset showed that the proposed method increased by 6.3% mAP and 20 FPS higher than YOLOv4, 7.6% mAP and 36 FPS higher than Faster-R-CNN, 5% mAP and 36 FPS higher than Mask-R-CNN, and 4.1% mAP and 35 FPS higher than DetectoRS. The results demonstrate that the DFPN can detect multi-target ships in different scenes with high accuracy and a fast detection speed.

TS2Anet: Ship detection network based on transformer

Ship positioning is critical to the reduction of marine casualties and the minimisation of losses at sea. However, most existing object detection methods use horizontal box detection, which fails to detect the direction of the ship. In addition, since ships have a specific orientation, the target box identified by the horizontal box often contains impurities. This makes it difficult to accurately detect the ship's coordinates. To address these limitations, this paper proposes the TS2Anet model, which uses the S2Anet rotating box object detection network and the PVTv2 structure as its backbone. In addition, to reduce the attention to outlier samples and improve the experimental performance, the image pre-processing method “cutout” was employed to simulate occlusion due to clouds, and the GHM function was used. With an increase in accuracy of 11.98% on the HRSC2016 dataset, the proposed model showed excellent detection performance. Furthermore, the model performed well in the coastal region of the HRSID dataset. This demonstrates the accuracy of ship detection by the proposed model.

ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion

To enhance ship detection accuracy in the presence of complex scenes and significant variations in object scales, this study introduces three enhancements to ReDet, resulting in a more powerful ship detection model called rotation-equivariant bidirectional feature fusion detector (ReBiDet). Firstly, the feature pyramid network (FPN) structure in ReDet is substituted with a rotation-equivariant bidirectional feature fusion feature pyramid network (ReBiFPN) to effectively capture and enrich multiscale feature information. Secondly, K-means clustering is utilized to group the aspect ratios of ground truth boxes in the dataset and adjust the anchor size settings accordingly. Lastly, the difficult positive reinforcement learning (DPRL) sampler is employed instead of the random sampler to address the scale imbalance issue between objects and backgrounds in the dataset, enabling the model to prioritize challenging positive examples. Through numerous experiments conducted on the HRSC2016 and DOTA remote sensing image datasets, the effectiveness of the proposed improvements in handling complex environments and small object detection tasks is validated. The ReBiDet model demonstrates state-of-the-art performance in remote sensing object detection tasks. Compared to the ReDet model and other advanced models, our ReBiDet achieves mAP improvements of 3.20, 0.42, and 1.16 on HRSC2016, DOTA-v1.0, and DOTA-v1.5, respectively, with only a slight increase of 0.82 million computational parameters.

An Effective Multi-Layer Attention Network for SAR Ship Detection

The use of deep learning-based techniques has improved the performance of synthetic aperture radar (SAR) image-based applications, such as ship detection. However, all existing methods have limited object detection performance under the conditions of varying ship sizes and complex background noise, to the best of our knowledge. In this paper, to solve both the multi-scale problem and the noisy background issues, we propose a multi-layer attention approach based on the thorough analysis of both location and semantic information. The solution works by exploring the richness of spatial information of the low-level feature maps generated by a backbone and the richness of semantic information of the high-level feature maps created by the same method. Additionally, we integrate an attention mechanism into the network to exclusively extract useful features from the input maps. Tests involving multiple SAR datasets show that our proposed solution enables significant improvements to the accuracy of ship detection regardless of vessel size and background complexity. Particularly for the widely-adopted High-Resolution SAR Images Dataset (HRSID), the new method provides a 1.3% improvement in the average precision for detection. The proposed new method can be potentially used in other feature-extraction-based classification, detection, and segmentation.

A Novel Intelligent Ship Detection Method Based on Attention Mechanism Feature Enhancement

The intelligent perception ability of the close-range navigation environment is the basis of autonomous decision-making and control of unmanned ships. In order to realize real-time perception of the close-range environment of unmanned ships, an enhanced attention mechanism YOLOv4 (EA-YOLOv4) algorithm is proposed. First of all, on the basis of YOLOv4, the convolutional block attention module (CBAM) is used to search for features in channel and space dimensions, respectively, to improve the model’s feature perception of ship targets. Then, the improved-efficient intersection over union (EIoU) loss function is used to replace the complete intersection over union (CIoU) loss function of the YOLOv4 algorithm to improve the algorithm’s perception of ships of different sizes. Finally, in the post-processing of algorithm prediction, soft non-maximum suppression (Soft-NMS) is used to replace the non-maximum suppression (NMS) of YOLOv4 to reduce the missed detection of overlapping ships without affecting the efficiency. The proposed method is verified on the large data set SeaShips, and the average accuracy rate of mAP0.5–0.95 reaches 72.5%, which is 10.7% higher than the original network YOLOv4, and the FPS is 38 frames/s, which effectively improves the ship detection accuracy while ensuring real-time performance.

Improved Ship Object Detection in Low-Illumination Environments Using RetinaMFANet

Video-based ship object detection has long been a popular research issue that has received attention in the water transportation industry. However, in low-illumination environments, such as at night or in fog, the water environment has a complex variety of light sources, video surveillance images are often accompanied by noise, and information on the details of objects in images is worsened. These problems cause high rates of false detection and missed detection when performing object detection for ships in low-illumination environments. Thus, this paper takes the detection of ship objects in low-illumination environments at night as the research object. The technical difficulties faced by object detection algorithms in low-illumination environments are analyzed, and a dataset of ship images is constructed by collecting images of ships (in the Nanjing section of Yangtze River in China) in low-illumination environments. In view of the outstanding performance of the RetinaNet model in general object detection, a new multiscale feature fusion network structure for a feature extraction module is proposed based on the same network architecture, in such a way that the extraction of more potential feature information from low-illumination images can be realized. In line with the feature detection network, the regression and classification detection network for anchor boxes is improved by means of the attention mechanism, guiding the network structure in the detection of object features. Moreover, the design and optimization of the augmentation of multiple random images and prior bounding boxes in the training process are also carried out. Finally, on the basis of experimental validation analysis, the optimized detection model was able to improve ship detection accuracy by 3.7% with a limited decrease in FPS (frames per second), and has better results in application.

Accurate Ship Detection Using Electro-Optical Image-Based Satellite on Enhanced Feature and Land Awareness

This paper proposes an algorithm that improves ship detection accuracy using preprocessing and post-processing. To achieve this, high-resolution electro-optical satellite images with a wide range of shape and texture information were considered. The developed algorithms display the problem of unreliable detection of ships owing to clouds, large waves, weather influences, and shadows from large terrains. False detections in land areas with image information similar to that of ships are observed frequently. Therefore, this study involves three algorithms: global feature enhancement pre-processing (GFEP), multiclass ship detector (MSD), and false detected ship exclusion by sea land segmentation image (FDSESI). First, GFEP enhances the image contrast of high-resolution electro-optical satellite images. Second, the MSD extracts many primary ship candidates. Third, falsely detected ships in the land region are excluded using the mask image that divides the sea and land. A series of experiments was performed using the proposed method on a database of 1984 images. The database includes five ship classes. Therefore, a method focused on improving the accuracy of various ships is proposed. The results show a mean average precision (mAP) improvement from 50.55% to 63.39% compared with other deep learning-based detection algorithms.

Lightweight and anchor-free frame detection strategy based on improved CenterNet for multiscale ships in SAR images

Ship detection using synthetic aperture radar (SAR) images has important applications in military and civilian fields, but the different sizes of the ship downgrade the detection accuracy of multiscale ships. Aiming at the problem of the poor accuracy and low efficiency of multiscale ship detection in complex scenes, this paper proposes a lightweight and anchor-free frame detection strategy for multiscale ships in SAR images. First, to deal with the problems of limited training samples, different sizes, attitudes, and angles of the ships in SAR images, a data augmentation strategy suitable for SAR images is adopted to expand the training space, followed by multiscale training to enhance the model generalization ability for multiscale ship detection. Second, a lightweight and anchor-free ship detection model based on the improved CenterNet is proposed, which abandons the dense anchor frame generation and extracts the key point of the ships for detection and positioning. Compared with the anchor frame-based detection method, this proposed detection model does not need to use the post-processing method to remove redundant anchor frames, and can accurately locate the center point of the ships with a better detection performance. Third, to reduce the model size and simplify the model parameters, a more lightweight network design is adopted in combination with the characteristics of SAR images. Hence, a residual network (ResNet) with fewer convolutional layers is constructed as the backbone network, and the cross-stage partial network (CSPNet) and spatial pyramid pooling (SPP) network are designed as the bottleneck network. The shallow ResNet can fully extract the SAR image features and reduce the training overfitting, and CSPNet and SPP can effectively combine the low-level image features to obtain the high-level features, reducing the model computation while at the same time enhancing the feature extraction ability. Finally, the evaluation index of the common objects in the context dataset is introduced, which can provide higher-quality evaluation results for ship detection accuracy and provide comprehensive evaluation indicators for multiscale ship detection. Experimental results show that the proposed strategy has the advantages of high detection efficiency, strong detection ability, and good generalization performance, which can achieve real-time and high-precision detection of the multiscale ship in complex SAR images.

Dual-Channel and Two-Stage Dehazing Network for Promoting Ship Detection in Visual Perception System

Maritime video surveillance of visual perception system has become an essential method to guarantee unmanned surface vessels (USV) traffic safety and security in maritime applications. However, when visual data are collected in a foggy marine environment, the essential optical information is often hidden in the fog, potentially resulting in decreased accuracy of ship detection. Therefore, a dual-channel and two-stage dehazing network (DTDNet) is proposed to improve the clarity and quality of the image to guarantee reliable ship detection under foggy conditions. Specifically, an upper and lower sampling structure is introduced to expand the original two-stage dehazing network into a two-channel network, to further capture the image features from different scale. Meanwhile, the attention mechanism is combined to provide different weights for different feature maps to maintain more image information. Furthermore, the perceptual function is constructed with the MSE-based loss function, so that it can better reduce the gap between the dehazing image and the unhazy image. Extensive experiments show that DTDNet has a better dehazing performance on both visual effects and quantitative index than other state-of-the-art dehazing networks. Moreover, the dehazing network is combined with the problem of ship detection under a sea-fog environment, and experiment results demonstrate that our network can be effectively applied to improve the visual perception performance of USV.

Multiscale ship detection based on cascaded dense weighted networks in synthetic aperture radar images

The sizes of ships vary by type in synthetic aperture radar (SAR) images, and this multiscale problem degrades the accuracy of ship detection. We present a multiscale ship detection method based on cascaded dense weighted networks in SAR images. Dense weighted fusion is performed in the feature extraction module and feature fusion module by cascading, i.e., feature weighting is achieved by embedding a convolutional block attention module after feature layers at different scales, followed by dense connection to fuse features at different scales. This makes it possible to refine the cascaded feature mapping, enhance the information transfer of shallow and deep features, and build a multiscale feature representation model. Experiments on the extended high-resolution SAR images dataset show that the accuracy of this method is 74.87% in inshore scenes, and 97.39% in offshore scenes, which exceeds that of other ship detection methods. In addition, in two SAR images from TerraSAR-X, the detection accuracy of multiscale ships reaches 86.63%, which indicates the method’s better robustness.

CAFC-Net: A Critical and Align Feature Constructing Network for Oriented Ship Detection in Aerial Images.

Ship detection is one of the fundamental tasks in computer vision. In recent years, the methods based on convolutional neural networks have made great progress. However, improvement of ship detection in aerial images is limited by large-scale variation, aspect ratio, and dense distribution. In this paper, a Critical and Align Feature Constructing Network (CAFC-Net) which is an end-to-end single-stage rotation detector is proposed to improve ship detection accuracy. The framework is formed by three modules: a Biased Attention Module (BAM), a Feature Alignment Module (FAM), and a Distinctive Detection Module (DDM). Specifically, the BAM extracts biased critical features for classification and regression. With the extracted biased regression features, the FAM generates high-quality anchor boxes. Through a novel Alignment Convolution, convolutional features can be aligned according to anchor boxes. The DDM produces orientation-sensitive feature and reconstructs orientation-invariant features to alleviate inconsistency between classification and localization accuracy. Extensive experiments on two remote sensing datasets HRS2016 and self-built ship datasets show the state-of-the-art performance of our detector.

Ship Detection of Polarimetric SAR Images Using a Nonlocal Spatial Information-Guided Method

Ship detection of polarimetric synthetic aperture radar (PolSAR) plays an important role in marine monitoring and ocean protection. Over the past years, local spatial information around pixels has been successfully applied to this task. However, few works have been done on PolSAR ship detection using the nonlocal spatial information (NSI). Within this context, we here propose one NSI-guided ship detection method PMR. Briefly speaking, the feature power difference (PD) is first constructed by computing the total power difference between the center pixel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> and its most similar nonlocal pixel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> within a 7×7 window. Then, the polarimetric feature reflection symmetry (RS) is introduced into PD to construct the method PMR (i.e., PD Multiply RS) for further enhancing the target-to-clutter ratio (TCR) and improving the ship detection accuracy. Experiments carried out on three real PolSAR datasets show that, in comparison with some other methods, especially the recently proposed local neighborhood information-based ship detector PWF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>N</i></sub> , PMR is more apt for ship detection. On average, its figure of merit (FoM) and TCR values respectively surpass PWF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>N</i></sub> 0.24 and 12.83 dB.

FRS-Net: An Efficient Ship Detection Network for Thin-Cloud and Fog-Covered High-Resolution Optical Satellite Imagery

According to statistics, more than 50% optical satellite images are covered by cloud or fog. Moreover, the cloud-cover rate is much higher in large bodies of water and nearby areas than inland areas due to the high amount of water evaporation and condensation. Therefore, ship detection from optical remote sensing images based on water surface analysis is more susceptible to cloud and fog interference, which affects the detection accuracy. In the time-sensitive application field of remote sensing images, due to the massive parameters in the large-scale network model, the detection speed is slow, and a lightweight detection model is commonly used. However, it is difficult for the lightweight detection model to achieve both high efficiency and accuracy for ship detection in a cloud-cover environment. To solve these problems, this manuscript proposes a lightweight algorithm called Fog Remote Sensing Ship Detection Network (FRS-Net) suitable for ship detection from remote sensing images in thin-cloud and fog covered environments. FRS-Net is developed based on the Deep Learning algorithm and can effectively improve ship detection accuracy under thin-cloud and fog cover. First, for the allocation strategy of anchor boxes, by using K-means clustering algorithm, FRS-Net simplifies the number of anchor boxes by utilizing the shape characteristics of the ship, which improves the time efficiency and the detection accuracy. Second, the FRS-Net network can meet the detection accuracy and has a fast inference speed. FRS-Net network is mainly composed of backbone extraction network, feature fusion network and prediction network. Experimental results on the Ship Detection in Optical Remote sensing images (SDIOR) dataset demonstrate the detection accuracy and computational efficiency of FRS-Net. The recognition mean Average Precision (mAP) achieved 43.20% for ship detection under thin-cloud and fog cover, with an efficiency of up to 424 FPS. FRS-Net has the potential to be applied in future scenarios such as embedded processing and on-board processing, where computing capabilities are strictly limited and the timeliness requirement is high.

Multi-Source Remote Sensing Image Fusion for Ship Target Detection and Recognition

The active recognition of interesting targets has been a vital issue for remote sensing. In this paper, a novel multi-source fusion method for ship target detection and recognition is proposed. By introducing synthetic aperture radar (SAR) sensor images, the proposed method solves the problem of precision degradation in optical remote sensing image target detection and recognition caused by the limit of illumination and weather conditions. The proposed method obtains port slice images containing ship targets by fusing optical data with SAR data. On this basis, spectral residual saliency and region growth method are used to detect ship targets in optical image, while SAR data are introduced to improve the accuracy of ship detection based on joint shape analysis and multi-feature classification. Finally, feature point matching, contour extraction and brightness saliency are used to detect the ship parts, and the ship target types are identified according to the voting results of part information. The proposed ship detection method obtained 91.43% recognition accuracy. The results showed that this paper provides an effective and efficient ship target detection and recognition method based on multi-source remote sensing images fusion.