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

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.

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Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3

Remote Sensing ◽

10.3390/rs12010044 ◽

2019 ◽

Vol 12 (1) ◽

pp. 44 ◽

Cited By ~ 10

Author(s):

Haojie Ma ◽

Yalan Liu ◽

Yuhuan Ren ◽

Jingxian Yu

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Detection Methods ◽

Remote Sensing Images ◽

Sensing Technology ◽

Collapsed Buildings ◽

Improved Model ◽

Detection Speed

An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model’s generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images.

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Airport Detection and Aircraft Recognition Based on Two-Layer Saliency Model in High Spatial Resolution Remote-Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2016.2620900 ◽

2017 ◽

Vol 10 (4) ◽

pp. 1511-1524 ◽

Cited By ~ 45

Author(s):

Libao Zhang ◽

Yingying Zhang

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Images ◽

Saliency Model ◽

Airport Detection

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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.

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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.

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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.

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Ship Detection in Multispectral Satellite Images Under Complex Environment

Remote Sensing ◽

10.3390/rs12050792 ◽

2020 ◽

Vol 12 (5) ◽

pp. 792 ◽

Cited By ~ 4

Author(s):

Xiaoyang Xie ◽

Bo Li ◽

Xingxing Wei

Keyword(s):

Remote Sensing ◽

Weather Conditions ◽

Detection Methods ◽

Multispectral Images ◽

Complex Environments ◽

Remote Sensing Images ◽

Ship Detection ◽

Additional Information ◽

Multispectral Satellite Images ◽

New Feature

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.

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