Ship Detection in Multispectral Satellite Images Under Complex Environment

Ship detection in multispectral remote-sensing images is critical in marine surveillance applications. The previously proposed ship-detection methods for multispectral satellite imagery usually work well under ideal conditions. When meeting complex environments such as shadows, mists, or clouds, they fail to detect ships. To solve this problem, we propose a novel spectral-reflectance-based ship-detection method. Research has shown that different materials have unique reflectance curves in the same spectral wavelength range. Based on this observation, we present a new feature using the reflectance gradient across multispectral bands. Moreover, we propose a neural network called lightweight fusion networks (LFNet). This network combines the aforementioned reflectance and the color information of multispectral images to jointly verify the regions with ships. The method utilizes a coarse-to-fine detection framework because of the large-sense-sparse-targets situation in remote-sensing images. In the coarse stage, the proposed reflectance feature vector is used to input the classifier to rule out the regions without ships. In fine detection, the LFNet is used to verify true ships. Compared with some traditional methods that merely depend on appearance features in images, the proposed method takes advantage of employing the reflectance variance in objects between each band as additional information. Extensive experiments have been conducted on multispectral images from four satellites under different weather and environmental conditions to demonstrate the effectiveness and efficiency of the proposed method. The results show that our method can still achieve good performance even under harsh weather conditions.

Download Full-text

Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

Sensors ◽

10.3390/s20092536 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2536 ◽

Cited By ~ 1

Author(s):

Jian He ◽

Yongfei Guo ◽

Hangfei Yuan

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Detection Efficiency ◽

Detection System ◽

Automatic Detection ◽

Sea Surface ◽

Detection Methods ◽

Remote Sensing Images ◽

Ship Detection ◽

Saliency Model

Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds.

Download Full-text

Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data

Open Geosciences ◽

10.1515/geo-2020-0180 ◽

2020 ◽

Vol 12 (1) ◽

pp. 1169-1184

Author(s):

Liang Zhong ◽

Xiaosheng Liu ◽

Peng Yang ◽

Rizhi Lin

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Infrared Imaging ◽

Detection Methods ◽

Detection Accuracy ◽

Constant False Alarm Rate ◽

Remote Sensing Images ◽

Ship Detection ◽

Application Potential ◽

Nighttime Light

AbstractNighttime light remote sensing images show significant application potential in marine ship monitoring, but in areas where ships are densely distributed, the detection accuracy of the current methods is still limited. This article considered the LJ1-01 data as an example, compared with the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data, and explored the application of high-resolution nighttime light images in marine ship detection. The radiation values of the aforementioned two images were corrected to achieve consistency, and the interference light sources of the ship light were filtered. Then, when the threshold segmentation and two-parameter constant false alarm rate methods are combined, the ships’ location information was with obtained, and the reliability of the results was analyzed. The results show that the LJ1-01 data can not only record more potential ship light but also distinguish the ship light and background noise in the data. The detection accuracy of the LJ1-01 data in both ship detection methods is significantly higher than that of the NPP/VIIRS data. This study analyzes the characteristics, performance, and application potential of the high-resolution nighttime light data in the detection of marine vessels. The relevant results can provide a reference for the high-precision monitoring of nighttime marine ships.

Download Full-text

A Coarse-to-Fine Network for Ship Detection in Optical Remote Sensing Images

Remote Sensing ◽

10.3390/rs12020246 ◽

2020 ◽

Vol 12 (2) ◽

pp. 246 ◽

Cited By ~ 5

Author(s):

Yue Wu ◽

Wenping Ma ◽

Maoguo Gong ◽

Zhuangfei Bai ◽

Wei Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Detection Methods ◽

False Alarms ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Ship Detection ◽

Detection Strategy ◽

Image Pixels ◽

Bounding Boxes ◽

Coarse To Fine

With the increasing resolution of optical remote sensing images, ship detection in optical remote sensing images has attracted a lot of research interests. The current ship detection methods usually adopt the coarse-to-fine detection strategy, which firstly extracts low-level and manual features, and then performs multi-step training. Inadequacies of this strategy are that it would produce complex calculation, false detection on land and difficulty in detecting the small size ship. Aiming at these problems, a sea-land separation algorithm that combines gradient information and gray information is applied to avoid false alarms on land, the feature pyramid network (FPN) is used to achieve small ship detection, and a multi-scale detection strategy is proposed to achieve ship detection with different degrees of refinement. Then the feature extraction structure is adopted to fuse different hierarchical features to improve the representation ability of features. Finally, we propose a new coarse-to-fine ship detection network (CF-SDN) that directly achieves an end-to-end mapping from image pixels to bounding boxes with confidences. A coarse-to-fine detection strategy is applied to improve the classification ability of the network. Experimental results on optical remote sensing image set indicate that the proposed method outperforms the other excellent detection algorithms and achieves good detection performance on images including some small-sized ships and dense ships near the port.

Download Full-text

Improved YOLOv3 Based on Attention Mechanism for Fast and Accurate Ship Detection in Optical Remote Sensing Images

Remote Sensing ◽

10.3390/rs13040660 ◽

2021 ◽

Vol 13 (4) ◽

pp. 660

Author(s):

Liqiong Chen ◽

Wenxuan Shi ◽

Dexiang Deng

Keyword(s):

Remote Sensing ◽

High Efficiency ◽

Attention Mechanism ◽

Detection Methods ◽

Detection Accuracy ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Ship Detection ◽

Maritime Surveillance ◽

Detection Speed

Ship detection is an important but challenging task in the field of computer vision, partially due to the minuscule ship objects in optical remote sensing images and the interference of clouds occlusion and strong waves. Most of the current ship detection methods focus on boosting detection accuracy while they may ignore the detection speed. However, it is also indispensable to increase ship detection speed because it can provide timely ocean rescue and maritime surveillance. To solve the above problems, we propose an improved YOLOv3 (ImYOLOv3) based on attention mechanism, aiming to achieve the best trade-off between detection accuracy and speed. First, to realize high-efficiency ship detection, we adopt the off-the-shelf YOLOv3 as our basic detection framework due to its fast speed. Second, to boost the performance of original YOLOv3 for small ships, we design a novel and lightweight dilated attention module (DAM) to extract discriminative features for ship targets, which can be easily embedded into the basic YOLOv3. The integrated attention mechanism can help our model learn to suppress irrelevant regions while highlighting salient features useful for ship detection task. Furthermore, we introduce a multi-class ship dataset (MSD) and explicitly set supervised subclass according to the scales and moving states of ships. Extensive experiments verify the effectiveness and robustness of ImYOLOv3, and show that our method can accurately detect ships with different scales in different backgrounds, while at a real-time speed.

Download Full-text

On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning

Remote Sensing ◽

10.3390/rs13101995 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1995

Author(s):

Pan Xu ◽

Qingyang Li ◽

Bo Zhang ◽

Fan Wu ◽

Ke Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Real Time ◽

Weather Conditions ◽

Processing Unit ◽

Constant False Alarm Rate ◽

Remote Sensing Images ◽

Detection Scheme ◽

Ship Detection ◽

Emergency Situations

Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis is not possible and real-time information for emergency situations is restricted. To solve this problem, this paper proposes an on-board ship detection scheme that is based on the traditional constant false alarm rate (CFAR) method and lightweight deep learning. This scheme can be used by the SAR satellite on-board computing platform to achieve near real-time image processing and data transmission. First, we use CFAR to conduct the initial ship detection and then apply the You Only Look Once version 4 (YOLOv4) method to obtain more accurate final results. We built a ground verification system to assess the feasibility of our scheme. With the help of the embedded Graphic Processing Unit (GPU) with high integration, our method achieved 85.9% precision for the experimental data, and the experimental results showed that the processing time was nearly half that required by traditional methods.

Download Full-text

Arbitrary Oriented Ship Detection in Optical Remote Sensing Images via Partially Supervised Learning

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188661 ◽

2020 ◽

Author(s):

Linhao Li ◽

Zhiqiang Zhou ◽

Lingjuan Miao ◽

Junfu Liu ◽

Xiaowu Xiao

Keyword(s):

Remote Sensing ◽

Supervised Learning ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Ship Detection ◽

Partially Supervised Learning

Download Full-text

Cnn Based Renormalization Method for Ship Detection in Vhr Remote Sensing Images

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518680 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tengfei Wang ◽

Yanfeng Gu

Keyword(s):

Remote Sensing ◽

Remote Sensing Images ◽

Ship Detection ◽

Renormalization Method

Download Full-text

Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

Remote Sensing ◽

10.3390/rs12010152 ◽

2020 ◽

Vol 12 (1) ◽

pp. 152 ◽

Cited By ~ 6

Author(s):

Ting Nie ◽

Xiyu Han ◽

Bin He ◽

Xiansheng Li ◽

Hongxing Liu ◽

...

Keyword(s):

Remote Sensing ◽

Fourier Transform ◽

Detection Method ◽

Saliency Detection ◽

Visual Saliency ◽

Saliency Map ◽

False Alarms ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

Download Full-text