Ship Target Segmentation Research Articles

Advanced integrated segmentation approach for semi-supervised infrared ship target identification

Infrared ship target segmentation is the important basis of infrared guided weapon in the sea-air context. Typically, accurate infrared ship target segmentation relies on a large number of pixel-level labels. However, it is difficult to obtain them. To this end, we present a method of Semi-supervised Infrared Ship Target Segmentation with Dual Branch (SeISTS-DB), which utilizes a small amount of labeled data and a large amount of unlabeled data to train model and improve segmentation performance. There are three main contributions. First, we design a target segmentation branch to generate the pseudo labels for unlabeled data. It consists of a dual learning network and a segmentation network. The dual learning network generates pseudo labels with weights for unlabeled data. The segmentation network is trained using both labeled data and unlabeled data with pseudo labels to achieve target segmentation of infrared ship, obtaining the preliminary segmentation results. Secondly, we introduce an error segmentation pixel correction branch, which contains a student network and a teacher network, to modify the pixel category error of the preliminary segmentation map. Finally, the outputs of the two branches are combined to obtain the final segmentation result. The SeISTS-DB is compared with other fully-supervised and semi-supervised methods on the infrared ship images dataset. Experimental results demonstrate that when the labeled data accounts for 1/8 of the training data, the mean Intersection over Union (mIou) is respectively improved by 15.35% and 6.19% at most. Besides, it is also compared with other methods on the public IRSTD-1k dataset, when the proportion of labeled images is 1/8, the mIoU is respectively improved by 11.76% at most compared to the state-of-the-art semi-supervised methods, demonstrating its effectiveness.

Instance segmentation ship detection based on improved Yolov7 using complex background SAR images

It is significant for port ship scheduling and traffic management to be able to obtain more precise location and shape information from ship instance segmentation in SAR pictures. Instance segmentation is more challenging than object identification and semantic segmentation in high-resolution RS images. Predicting class labels and pixel-wise instance masks is the goal of this technique, which is used to locate instances in images. Despite this, there are now just a few methods available for instance segmentation in high-resolution RS data, where a remote-sensing image’s complex background makes the task more difficult. This research proposes a unique method for YOLOv7 to improve HR-RS image segmentation one-stage detection. First, we redesigned the structure of the one-stage fast detection network to adapt to the task of ship target segmentation and effectively improve the efficiency of instance segmentation. Secondly, we improve the backbone network structure by adding two feature optimization modules, so that the network can learn more features and have stronger robustness. In addition, we further modify the network feature fusion structure, improve the module acceptance domain to increase the prediction ability of multi-scale targets, and effectively reduce the amount of model calculation. Finally, we carried out extensive validation experiments on the sample segmentation datasets HRSID and SSDD. The experimental comparisons and analyses on the HRSID and SSDD datasets show that our model enhances the predicted instance mask accuracy, enhancing the instance segmentation efficiency of HR-RS images, and encouraging further enhancements in the projected instance mask accuracy. The suggested model is a more precise and efficient segmentation in HR-RS imaging as compared to existing approaches.

Infrared ship target segmentation based on Adversarial Domain Adaptation

Infrared ship target segmentation is one of the key technologies for automatically detecting ship targets in ocean monitoring. However, it is a challenging work to achieve accurate target segmentation from the infrared ship image. To improve its segmentation performance, we present an Adversarial Domain Adaptation Network (ADANet) for infrared ship target segmentation, where the labeled visible ship images are used as the source domain and the unlabeled infrared ship images are as the target domain. To address the issue of style difference between the two domains, we preprocess the visible images of the source domain in turn with graying and whitening to convert them into the images with the style of the target domain. For the infrared images in the target domain, we optimize them with a denoising network. Furthermore, to solve the matter of limited receptive field of the discriminator, we design a discriminator based on atrous convolution to improve its discriminative ability. Finally, for the issue of low confidence of the target domain predicted images, we add the information entropy of the target domain predicted images to the adversarial loss. Experimental results on the home-made dataset as well as a public dataset show that infrared ship target segmentation achieves higher mean intersection over union than the state-of-the-art methods without significantly increase of parameters, demonstrating its effectiveness.

Two-Stage Domain Adaptation for Infrared Ship Target Segmentation

Two-Stage Domain Adaptation for Infrared Ship Target Segmentation

Ship Target Segmentation for SAR Images Based on Clustering Center Shift

Ship target segmentation plays an important role in synthetic aperture radar (SAR) image interpretation. However, existing segmentation methods for marine SAR images have the problem of inaccurate edge segmentation, a concern for real-world applications. In this letter, we propose a clustering center shifted adaptive target segmentation (CCSATS) method. Firstly, the proposed clustering center shift method is used to update the clustering centers of each iteration, which can quickly and accurately capture ship pixels. Then, based on regional homogeneity coefficients, we define a new similarity measurement criterion with two adaptive weight factors to ensure the homogeneity of segmentation results. Finally, neighborhood patches are used to represent pixel information, which can reduce the influence of speckle noise and enhance the target edge fitting ability. Our segmentation results of measured SAR images show that the proposed method effectively ensures segmentation accuracy. Compared with other existing methods, the proposed target segmentation method achieves better edge capture performance.

A Multi-Task CNN for Maritime Target Detection

In this letter, we construct MaRine ShiP (MRSP-13), a novel dataset containing 37,161 ship target images belonging to 13 classes with bounding box annotation, and among them there are 3051 images labeled with pixel-level annotation. This dataset equips us with the capability to conduct baseline experiments on maritime target classification, detection and segmentation. We propose a cross-layer multi-task CNN model for maritime target detection, which can simultaneously solve ship target detection, classification, and segmentation. Experimental results have demonstrated the efficiency of the MRSP-13 dataset to be used for maritime target analysis. In addition, the results validate the fact that by adopting the strategies of feature sharing, joint learning, and cross-layer connections, the proposed model achieves superior performance with less annotations. We believe that our MRSP-13 dataset and corresponding baseline experiments will lay down the foundation for further research in maritime target processing.

Automatic Infrared Ship Target Segmentation Based on Structure Tensor and Maximum Histogram Entropy

The existing infrared (IR) ship target segmentation methods may suffer serious performance degradation in the situation of diverse background clutters and ship targets. To cope with this problem, a novel ship target segmentation method is proposed in this paper. Initially, the IR image is transformed into the map of large eigenvalues of structure tensor (STLE), where the horizon line and ship target boundary can be explicitly characterized. According to the scene context clue, the automatic horizon line detection (AHLD) is proposed to efficiently judge the existence of horizon line and remove sky/land region clutters. Then, based on the intensity distribution of ship target and sea background, the adaptive maximum histogram entropy (AMHE) is presented to accurately perceive the brightness (dark or bright) of ship target, and coarsely segment the bright or dark ship target from sea background. After that, considering the ship target boundary information, the regions-of-interest (ROI) of ship target is located and the ship foreground map (SFM) is developed to address the under-segmentation. Finally, a new Watershed algorithm namely structure tensor and maximum histogram entropy modified Watershed transform (TEWT) is constructed to completely extract the whole ship target. Extensive experiments show that the proposed method outperforms the state-of-the-art methods, especially for IR images with intricate background clutter and heavy noise. Moreover, the proposed method can work stably for ship target with unknown brightness, uneven intensities, low contrast, variable quantities, sizes, and shapes.

Ship target detection and segmentation method based on multi‐fractal analysis

A novel automatic ship target detection and segmentation approach based on modified multi-fractal method in synthetic aperture radar (SAR) image is proposed in this study. Firstly, singular power spectrum (SPS) is developed to two-dimensional SPS (2D-SPS), and applied to feature extraction and SAR target detection. Secondly, 2D Holder exponent and 2D multi-fractal spectrum are jointly used to ship target segmentation in SAR image. The algorithm steps and detection flow is here conceptually assessed, analytically derived, numerically verified, and also tested on actual SAR images. Results indicate that the proposed method has significant advantages in ship target detection and segmentation in SAR image.

Rotationally Unconstrained Region Proposals for Ship Target Segmentation in Optical Remote Sensing

The segmentation of ship targets in remote sensing images is of great importance in both military and civil fields. The existing methods often suffer from a low overlap between region proposals, which are constrained to be the horizontal rectangles and the ground truth. This enhances the problems of background noise and results in more false positives and a lower recall. Hence, we propose a method to generate the rotated bounding boxes that are more appropriate for ship segmentation. The proposed method is based on the Mask R-CNN framework, and the key contribution lies in the approach to predefine the rotated bounding boxes and generate the rotationally unconstrained region proposals. Specifically, for a double-stage proposal generation, a multiangle region proposal layer is designed; following this, an adapted alignment approach is proposed to extract the features for each proposal. In addition to the above-mentioned two proposed methods, we adopt a second stage to refine the proposals with rotation regression and mask prediction. We evaluated the proposed method using a remote sensing dataset with extensively labeled ship targets, and the experimental results show that the proposed method performs better than its competitors. The proposed method significantly increases the recall of densely arranged ships while substantially reducing the number of false positives.

Ship Target Segmentation in SAR Images Using A Modified Nonlocal Active Contour

In ocean surveillance, ship targets are the focus of attention. The ship target segmentation of SAR images is still challenging due to not only speckle noise but also the variations of backscattering intensities. A new segmentation method for ship targets is proposed in this paper, based on the nonlocal processing principle. The level set is implemented by using nonlocal comparison of patches. In the procedure of pairwise interaction, only the local homogeneity of image features is utilized, and this is important in optimizing regions with homogeneous features. In order to make the method applicable for SAR images, a distribution metric is incorporated into the energy function to suppress the multiplicative noise. Experimental results based on TerraSAR data illustrate the effective performance of the proposed method on the ship target segmentation: It can overcome the speckle noise and intensity variations.

A convex active contour model driven by local entropy energy with applications to infrared ship target segmentation

To segment ship target in infrared (IR) images, a convex active contour model based on local image entropy is proposed in this paper. Firstly, local image entropy based on kernel function is obtained; on this basis, the convex energy functional is built using the variational level set method in the convex set. Because of the introduction of entropy, the proposed method can protect the image edge and enhance the ability to deal images with heterogeneity. At the same time, the convex energy functional can get a global minimum and has robustness against initial curve placement. Compared with the state-of-the-art methods, experiment results demonstrate the performance and effectiveness of the proposed method.

Adaptable active contour model with applications to infrared ship target segmentation

Active contour model is widely and popularly used in the field of image segmentation because of its superior theoretical properties and efficient numerical methods. An algorithm to segment a ship target in infrared (IR) images using Chan–Vese (C–V) active contour model is proposed here. The method effectively integrates both image regional and boundary information by an adaptable weight function. The method can segment IR ship images, which usually contain noises, blurry boundaries, and heterogeneous regions. In addition, compared with the state-of-the-art methods, experiment results demonstrate the performance and effectiveness of this method.

Infrared ship target segmentation through integration of multiple feature maps

We investigate the issue of ship target segmentation in infrared (IR) images, and propose an efficient method based on feature map integration. It consists of mainly two procedures: salient region detection based on multiple feature map integration and salient region segmentation based on locally adaptive thresholding. Firstly, a saliency map is constructed by integrating multiple features of IR ship targets, including gray level intensity, local contrast, salient linear structures, and edge strength. Secondly, we propose an adaptive thresholding method to segment each local salient region, and a target selection procedure based on shape features is used to remove background and obtain the true target. Experimental results show that the proposed method performs well for IR ship target segmentation. The advantage of the proposed method is demonstrated in both visual and quantitative comparisons, especially for IR images with a bright background or a ship target close to port.

Infrared Ship Target Segmentation Based on Spatial Information Improved FCM.

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The fuzzy C-means (FCM) clustering is a classical method widely used in image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM method based on the spatial information is proposed for IR ship target segmentation. The improvements include two parts: 1) adding the nonlocal spatial information based on the ship target and 2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov random field. In addition, the results of K -means are used to initialize the improved FCM method. Experimental results show that the improved method is effective and performs better than the existing methods, including the existing FCM methods, for segmentation of the IR ship images.

Iterative infrared ship target segmentation based on multiple features

This paper presents an efficient method for ship target segmentation in infrared (IR) images. It consists of mainly two procedures: iterative image segmentation and ship target selection. First, based on the intensity distribution of an IR image, we design a global background subtraction filter (GBSF) to suppress the background, and an adaptive row mean subtraction filter (ARMSF) to enhance the target. After iteratively applying these two filters, we can obtain a proper threshold for image segmentation. Second, based on the geometric properties of the ship target, we construct four shape features and a selection criterion to identify the real target and remove the non-target regions. Experimental results demonstrate that the proposed method can effectively segment ship targets from different backgrounds in IR images. The advantage of the proposed method over the others in the previous literatures is validated in both visual and quantitative comparisons, especially for IR images with low contrast and uneven intensities.

Unified mean shift segmentation and graph region merging algorithm for infrared ship target segmentation

We propose a unified approach that incorporates the mean shift-based image segmentation algorithm and the SST (shortest spanning tree)-minmax-based graph grouping method to achieve effective IR object segmentation performance amenable for real-time application. It preprocesses an image by using the mean shift algorithm to form segmented regions that can not only remove the noise, but also preserve the desirable discontinuity characteristics of the ship object. The segmented regions can then effectively represent the original image by using the graph structures, and we apply the SST-minmax method to perform merging procedure to form the final segmented regions. Due to the good discontinuity-preserving filtering characteristic, we can effectively remove the clutter disturbance of the sea background without loss of the IR ship object information, and significantly reduce the number of basic image entities. Therefore, the region merging based on SST-minmax can produce excellent segmentation performance at low computational cost due to smaller clutter disturbance and less region nodes. The superiority of the proposed method is examined and demonstrated through a large number of experiments using a real IR ship image sequence.