Inshore Ship Research Articles

A Data Augmentation Strategy Based on Simulated Samples for Ship Detection in RGB Remote Sensing Images

In this paper, we propose a data augmentation method for ship detection. Inshore ship detection using optical remote sensing imaging is a challenging task owing to an insufficient number of training samples. Although the multilayered neural network method has achieved excellent results in recent research, a large number of training samples is indispensable to guarantee the accuracy and robustness of ship detection. The majority of researchers adopt such strategies as clipping, scaling, color transformation, and flipping to enhance the samples. Nevertheless, these methods do not essentially increase the quality of the dataset. A novel data augmentation strategy was thus proposed in this study by using simulated remote sensing ship images to augment the positive training samples. The simulated images are generated by true background images and three-dimensional models on the same scale as real ships. A faster region-based convolutional neural network (Faster R-CNN) based on Res101netwok was trained by the dataset, which is composed of both simulated and true images. A series of experiments is designed under small sample conditions; the experimental results show that better detection is obtained with our data augmentation strategy.

Automatic Inshore Ship Detection in Satellite Imageries Using DWT and SIFT Features

Automatic Inshore Ship Detection in Satellite Imageries Using DWT and SIFT Features

Ship Detection for Optical Remote Sensing Images Based on Visual Attention Enhanced Network.

Ship detection plays a significant role in military and civil fields. Although some state-of-the-art detection methods, based on convolutional neural networks (CNN) have certain advantages, they still cannot solve the challenge well, including the large size of images, complex scene structure, a large amount of false alarm interference, and inshore ships. This paper proposes a ship detection method from optical remote sensing images, based on visual attention enhanced network. To effectively reduce false alarm in non-ship area and improve the detection efficiency from remote sensing images, we developed a light-weight local candidate scene network(CSN) to extract the local candidate scenes with ships. Then, for the selected local candidate scenes, we propose a ship detection method, based on the visual attention DSOD(VA-DSOD). Here, to enhance the detection performance and positioning accuracy of inshore ships, we both extract semantic features, based on DSOD and embed a visual attention enhanced network in DSOD to extract the visual features. We test the detection method on a large number of typical remote sensing datasets, which consist of Google Earth images and GaoFen-2 images. We regard the state-of-the-art method [sliding window DSOD (SW+DSOD)] as a baseline, which achieves the average precision (AP) of 82.33%. The AP of the proposed method increases by 7.53%. The detection and location performance of our proposed method outperforms the baseline in complex remote sensing scenes.

Saliency-Aware Convolution Neural Network for Ship Detection in Surveillance Video

Real-time detection of inshore ships plays an essential role in the efficient monitoring and management of maritime traffic and transportation for port management. Current ship detection methods which are mainly based on remote sensing images or radar images hardly meet real-time requirement due to the timeliness of image acquisition. In this paper, we propose to use visual images captured by an on-land surveillance camera network to achieve real-time detection. However, due to the complex background of visual images and the diversity of ship categories, the existing convolution neural network (CNN) based methods are either inaccurate or slow. To achieve high detection accuracy and real-time performance simultaneously, we propose a saliency-aware CNN framework for ship detection, comprising comprehensive ship discriminative features, such as deep feature, saliency map, and coastline prior. This model uses CNN to predict the category and the position of ships and uses the global contrast based salient region detection to correct the location. We also extract coastline information and respectively incorporate it into CNN and saliency detection to obtain more accurate ship locations. We implement our model on Darknet under CUDA 8.0 and CUDNN V5 and use a real-world visual image dataset for training and evaluation. The experimental results show that our model outperforms representative counterparts (Faster R-CNN, SSD, and YOLOv2) in terms of accuracy and speed.

Inshore Ship Detection Based on Level Set Method and Visual Saliency for SAR Images.

Inshore ship detection is an important research direction of synthetic aperture radar (SAR) images. Due to the effects of speckle noise, land clutters and low signal-to-noise ratio, it is still challenging to achieve effective detection of inshore ships. To solve these issues, an inshore ship detection method based on the level set method and visual saliency is proposed in this paper. First, the image is fast initialized through down-sampling. Second, saliency map is calculated by improved local contrast measure (ILCM). Third, an improved level set method based on saliency map is proposed. The saliency map has a higher signal-to-noise ratio and the local level set method can effectively segment images with intensity inhomogeneity. In this way, the improved level set method has a better segmentation result. Then, candidate targets are obtained after the adaptive threshold. Finally, discrimination is employed to get the final result of ship targets. The experiments on a number of SAR images demonstrate that the proposed method can detect ship targets with reasonable accuracy and integrity.

Inshore Ship Detection Based on Convolutional Neural Network in Optical Satellite Images

In this paper, we present a novel inshore ship detection method based on convolutional neural network (CNN). Different from current inshore ship detection methods that need complex shape and texture analysis or sea and land segmentation, our method starts from a global search for the relatively distinct ship head with an efficient classification network. This can help to obtain the location of possible ship heads as well as the rough ship directions, which are beneficial to generate smaller and more precise candidate regions of ship targets. Compared with other region proposal methods, our method can produce a rather smaller set of proposals. Next, iterative bounding-box regression and classification are unified into a multitask network, which is constructed and trained specially by considering the practical condition of the inshore ships in remote sensing images. At last, nonmaximum suppression is applied to eliminate duplicate detections. Experiments on optical satellite images demonstrate the effectiveness and robustness of the proposed method for inshore ship detection.

Object Detection Using Convolutional Neural Networks in a Coarse-to-Fine Manner

Object detection in remote sensing images has long been studied, but it remains challenging due to the diversity of objects and the complexity of backgrounds. In this letter, we propose an object detection method using convolutional neural networks (CNNs) in a coarse-to-fine manner. In the coarse step, coarse candidate regions that may contain objects are proposed. In the fine step, fine candidate regions are cropped from coarse candidate regions, and are classified as objects or backgrounds. We design a concise and efficient framework that can propose fewer candidate regions and extract more discriminative features. The framework consists of two eight-layer CNNs that are well designed and powerful. To use CNNs to detect inshore ships, image samples are required, each of which should contain only one ship. However, the traditional image cropping method cannot generate such samples. To solve this problem, we present an orientation-free image cropping method that can generate trapezium rather than rectangle samples, making inshore ship detection by CNN feasible. Experimental results on Google Earth images demonstrate that the proposed method outperforms existing state-of-the-art methods.

Fully Convolutional Network With Task Partitioning for Inshore Ship Detection in Optical Remote Sensing Images

Ship detection in optical remote sensing imagery has drawn much attention in recent years, especially with regards to the more challenging inshore ship detection. However, recent work on this subject relies heavily on hand-crafted features that require carefully tuned parameters and on complicated procedures. In this letter, we utilize a fully convolutional network (FCN) to tackle the problem of inshore ship detection and design a ship detection framework that possesses a more simplified procedure and a more robust performance. When tackling the ship detection problem with FCN, there are two major difficulties: 1) the long and thin shape of the ships and their arbitrary direction makes the objects extremely anisotropic and hard to be captured by network features and 2) ships can be closely docked side by side, which makes separating them difficult. Therefore, we implement a task partitioning model in the network, where layers at different depths are assigned different tasks. The deep layer in the network provides detection functionality and the shallow layer supplements with accurate localization. This approach mitigates the tradeoff of FCN between localization accuracy and feature representative ability, which is of importance in the detection of closely docked ships. The experiments demonstrate that this framework, with the advantages of FCN and the task partitioning model, provides robust and reliable inshore ship detection in complex contexts.

A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images

With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency.

Integrated Localization and Recognition for Inshore Ships in Large Scene Remote Sensing Images

Automatic inshore ship recognition, which includes target localization and type recognition, is an important and challenging task. However, existing ship recognition methods mainly focus on the classification of ship samples or clips. These methods rely deeply on the detection algorithm to complete localization and recognition in large scene images. In this letter, we present an integrated framework to automatically locate and recognize inshore ships in large scene satellite images. Different from traditional object recognition methods using two steps of detection-classification, the proposed framework could locate inshore ships and identify types without the detection step. Considering ship size is a useful feature, a novel multimodel method is proposed to utilize this feature. And an Euclidean-distance-based fusion strategy is used to combine candidates given by models. This fusion strategy could effectively separate side-by-side ships. To handle large scene images efficiently, scale-invariant feature transform registration is also integrated into the framework to utilize geographic information. All of these make the framework an end-to-end fashion which could automatically recognize inshore ships in large scene satellite images. Experiments on Quickbird images show that this framework could achieve the actual applied requirements.

Inshore Ship Detection in Remote Sensing Images via Weighted Pose Voting

Inshore ship detection from high-resolution satellite images is a useful yet challenging task in remote surveillance and military reconnaissance. It is difficult to detect the inshore ships with high precision because various interferences are present in the harbor scene. An inshore ship detection method based on the weighted voting and rotation–scale-invariant pose is proposed to improve the detection performance. The proposed method defines the rotation angle pose and the scaling factor of the detected ship to detect the ship with different directions and different sizes. For each pixel on the ship template, the possible poses of a detection window are estimated according to all possible pose-related pixels. To improve robustness to the shape-similar distractor and various interferences, the score of the detection window is obtained by designing a pose weighted voting method. Moreover, the values of some parameters such as similarity threshold and the weight of “V” are investigated. The experimental results on actual satellite images demonstrate that the proposed method is invariant to rotation and scale and robust in the inshore ship detection. In addition, better detection performance is observed in comparison with the existing inshore ship detection algorithms in terms of precision rate and recall rate. The target pose of the detected ship can also be obtained as a byproduct of the ship detection.

Inshore Ship Detection via Saliency and Context Information in High-Resolution SAR Images

So far, the detection of ships on sea has been widely studied, while fewer works are available on inshore ship detection. Due to the high similarity between the harbor and the ship body on gray and texture features, the traditional methods are unable to achieve effective detection of inshore ships. In this letter, we present a novel approach via saliency and context information to deal with this issue. First, we employ the superpixel-generating algorithm to generate the superpixel region. Second, we propose a new method based on the salient region detection to complete the inshore ship detection. Finally, a ship discrimination framework is presented to remove the false alarm. The experimental results demonstrate the noise robustness and effectiveness of the proposed approach using synthetic aperture radar (SAR) images and real SAR images.

A Novel Inshore Ship Detection via Ship Head Classification and Body Boundary Determination

In this letter, we propose a novel method for inshore ship detection via ship head classification and body boundary determination. Compared with some traditional ship head detection methods depending on accurate ship head segmentation, we generate novel ship head features in the transformed domain of polar coordinate, where the ship heads have an approximate trapezoid shape and can be more easily detected. Then, these features are used in the classification based on support vector machine to detect the ship head candidates, and give the important information of initial ship head direction. Next, the surrounding consistent line segments are utilized to refine the ship direction, and the ship boundary is determined based on the saliency of directional gradient information symmetrical about the ship body. Finally, the context information of sea areas is introduced to remove false alarms. Experimental results show that the proposed method can accurately and robustly detect the inshore ships in high-resolution optical remote sensing images.

Inshore ship detection using high-resolution synthetic aperture radar images based on maximally stable extremal region

We present a method for detecting inshore ships using high-resolution synthetic aperture radar (SAR) images. The method first applies preprocessing steps to obtain the solving region of an SAR image, suitable for the berthing rules. Then the ship candidates are extracted based on a maximally stable extremal region detector, while ship detection is applied using an improved constant false alarm rate (CFAR) detector. Finally, based on discrimination processing, false alarms are removed according to compactness, therefore the ultimate detection result is obtained without interference from strong terrestrial clutter or man-made objects. Experimental results show that this method could achieve effective detection of ships in harbors using high-resolution SAR images. This method can reduce the required computation time by 99.7%, compared with the traditional CFAR method.

Automatic Detection of Inshore Ships in High-Resolution Remote Sensing Images Using Robust Invariant Generalized Hough Transform

In this letter, we propose a new detection framework based on robust invariant generalized Hough transform (RIGHT) to solve the problem of detecting inshore ships in high-resolution remote sensing imagery. The invariant generalized Hough transform is an effective shape extraction technique, but it is not adaptive to shape deformation well. In order to improve its adaptability, we use an iterative training method to learn a robust shape model automatically. The model could capture the shape variability of the target contained in the training data set, and every point in the model is equipped with an individual weight according to its importance, which greatly reduces the false-positive rate. Through the iteration process, the model performance is gradually improved by extending the shape model with these necessary weighted points. Experimental result demonstrates the precision, robustness, and effectiveness of our detection framework based on RIGHT.

Method for inshore ship detection based on feature recognition and adaptive background window

Inshore ship detection in synthetic aperture radar (SAR) images is a challenging task. We present an inshore ship detection method based on the characteristics of inshore ships. We first use the Markov random field (MRF) method to segment water and land, and then extract the feature points of inshore ships using polygonal approximation. Following this, we propose new rules for inshore ship extraction and use these rules to separate inshore ships from the land in binary images. Finally, we utilize the adaptive background window (ABW) to complete the clutter statistic and successfully detect inshore ships using a constant false alarm rate (CFAR) detector with ABW and G0 distribution. Experimental results using SAR images show that our method is more accurate than traditional CFAR detection based on K-distribution (K-CFAR), given the same CFAR, and that the quality of the image obtained through our method is higher than that of the traditional K-CFAR detection method by a factor of 0.165. Our method accurately locates and detects inshore ships in complicated environments and thus is more practical for inshore ship detection.

A New Method on Inshore Ship Detection in High-Resolution Satellite Images Using Shape and Context Information

In this letter, we present a new method to detect inshore ships using shape and context information. We first propose a new energy function based on an active contour model to segment water and land and minimize it with an iterative global optimization method. The proposed energy performs well on the different intensity distributions between water and land and produces a result that can be well used in shape and context analyses. In the segmented image, ships are detected with successive shape analysis, including shape analysis in the localization of ship head and region growing in computing the width and length of ship. Finally, to locate ships accurately and remove the false alarms, we unify them with a binary linear programming problem by utilizing the context information. Experiments on QuickBird images show the robustness and precision of our method.

An AIAC-based Inshore Ship Target Detection Approach

In high spatial resolution sensing image,it was hard to discriminate inshore ships from harbors because of the similarity of gray and texture for both targets. The half ellipse shape of the inshore ship in the contour was one of the features with better distinguish ability.An alterable included angle chain(AIAC)-based curve description was introduced to describe the inshore ship contours in this paper,which was invariant to rotation,scaling and translation.In the following an improved AIAC generation method was presented.Finally an AIAC-based inshore ship detection approach was proposed by describing and matching the counter feature of inshore ship by AIAC.Experiments based on the satellite sensing image demonstrated that the algorithm proposed could solve the inshore ship detection problem in a certain extent.

A comparison of ship and satellite chlorophyll from California and Peru

Ship surface chlorophyll measurements from coastal California and Peru have been processed for comparison with surface chlorophyll estimated during the lifetime of the coastal zone color scanner (CZCS). The ship data shows that chlorophyll concentrations are higher off Peru than California, consistent with the Peru Current system's being more productive. Off California the ship and satellite measurements yield similar estimates of chlorophyll concentration (the average ratio of ship to satellite is 1.1), but off Peru the satellite yields substantially lower concentrations (average ratio of 3.8). The lower satellite values off Peru are likely due to sparse satellite coverage and/or problems with atmospheric corrections. For California, the inshore (0–100 km) ship and satellite chlorophyll estimates are similar, but offshore (200–250 km) the satellite yields higher values. Both estimates, however, confirm the presence of a winter chlorophyll maximum in offshore California waters (likely due to increased nutrient concentrations), increases in chlorophyll per cell, and mixing of subsurface chlorophyll maximum to the surface. Conversely, off Peru, offshore ship and satellite values are in closer agreement than inshore values. As a result onshore‐offshore gradients are weaker in the satellite data for both California and Peru. The gradient problem is likely the result of the satellite algorithm. Different algorithms may be needed for coastal waters, dominated by relatively large and colonial phytoplankton, and open ocean waters, dominated by small solitary phytoplankton.

目的地までの最適経路法 : 近海域における小型船の場合

We studied optimal routing to the destination when inshore ships will encounter rough sea. The route is to get minimum-time paths through a region of position-dependent vector velocity by optimization for dynamics systems. The ship speed reduction by wave height and direction in addition to ocean current was taken in this paper. The data area is 400 miles from east to west and 300 miles from north to south. The lattice points are divided into every 50 miles and 30 miles along X and Y-axis. These data are based on wave prognosis chart and sea surface current chart near Japan by weather facsimile. We are comparing the optimal routing with the direct routing to prove the validity of this method.