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

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.

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

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.

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

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2536 ◽  
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.

scholarly journals Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion

2020 ◽  
Vol 12 (20) ◽  
pp. 3316 ◽  
Author(s):  
Yulian Zhang ◽  
Lihong Guo ◽  
Zengfa Wang ◽  
Yang Yu ◽  
Xinwei Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Atmospheric Correction ◽  
Recall Rate ◽  
Google Earth ◽  
Superior Performance ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Ship Detection

Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image.

scholarly journals A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China

2020 ◽  
Vol 12 (14) ◽  
pp. 2334
Author(s):  
Lu Zhao ◽  
Hongyan Ren ◽  
Cheng Cui ◽  
Yaohuan Huang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Uv Detection ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Detection Model ◽  
Model Object ◽  
Object Based ◽  
Similar Area ◽  
Urban Villages

High-resolution remotely sensed imageries have been widely employed to detect urban villages (UVs) in highly urbanized regions, especially in developing countries. However, the understanding of the potential impacts of spatially and temporally differentiated urban internal development on UV detection is still limited. In this study, a partition-strategy-based framework integrating the random forest (RF) model, object-based image analysis (OBIA) method, and high-resolution remote sensing images was proposed for the UV-detection model. In the core regions of Guangzhou, four original districts were re-divided into five new zones for the subsequent object-based RF-detection of UVs with a series features, according to the different proportion of construction lands. The results show that the proposed framework has a good performance on UV detection with an average overall accuracy of 90.23% and a kappa coefficient of 0.8. It also shows the possibility of transferring samples and models into a similar area. In summary, the partition strategy is a potential solution for the improvement of the UV-detection accuracy through high-resolution remote sensing images in Guangzhou. We suggest that the spatiotemporal process of urban construction land expansion should be comprehensively understood so as to ensure an efficient UV-detection in highly urbanized regions. This study can provide some meaningful clues for city managers identifying the UVs efficiently before devising and implementing their urban planning in the future.

scholarly journals A Mixed Property-Based Automatic Shadow Detection Approach for VHR Multispectral Remote Sensing Images

2018 ◽  
Vol 8 (10) ◽  
pp. 1883 ◽  
Author(s):  
Hongyin Han ◽  
Chengshan Han ◽  
Xucheng Xue ◽  
Changhong Hu ◽  
Liang Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Near Infrared ◽  
Shadow Detection ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Multispectral Remote Sensing ◽  
The Difference ◽  
Mixed Property ◽  
Very High

Shadows in very high-resolution multispectral remote sensing images hinder many applications, such as change detection, target recognition, and image classification. Though a wide variety of significant research has explored shadow detection, shadow pixels are still more or less omitted and are wrongly confused with vegetation pixels in some cases. In this study, to further manage the problems of shadow omission and vegetation misclassification, a mixed property-based shadow index is developed for detecting shadows in very high-resolution multispectral remote sensing images based on the difference of the hue component and the intensity component between shadows and nonshadows, and the difference of the reflectivity of the red band and the near infrared band between shadows and vegetation cover in nonshadows. Then, the final shadow mask is achieved, with an optimal threshold automatically obtained from the index image histogram. To validate the effectiveness of our approach for shadow detection, three test images are selected from the multispectral WorldView-3 images of Rio de Janeiro, Brazil, and are tested with our method. When compared with other investigated standard shadow detection methods, the resulting images produced by our method deliver a higher average overall accuracy (95.02%) and a better visual sense. The highly accurate data show the efficacy and stability of the proposed approach in appropriately detecting shadows and correctly classifying shadow pixels against the vegetation pixels for very high-resolution multispectral remote sensing images.

scholarly journals Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images

2020 ◽  
Vol 9 (6) ◽  
pp. 370
Author(s):  
Atakan Körez ◽  
Necaattin Barışçı ◽  
Aydın Çetin ◽  
Uçman Ergün
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Mean Average Precision ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Average Precision ◽  
Proposed Model ◽  
Detection Of Objects ◽  
Very High

The detection of objects in very high-resolution (VHR) remote sensing images has become increasingly popular with the enhancement of remote sensing technologies. High-resolution images from aircrafts or satellites contain highly detailed and mixed backgrounds that decrease the success of object detection in remote sensing images. In this study, a model that performs weighted ensemble object detection using optimized coefficients is proposed. This model uses the outputs of three different object detection models trained on the same dataset. The model’s structure takes two or more object detection methods as its input and provides an output with an optimized coefficient-weighted ensemble. The Northwestern Polytechnical University Very High Resolution 10 (NWPU-VHR10) and Remote Sensing Object Detection (RSOD) datasets were used to measure the object detection success of the proposed model. Our experiments reveal that the proposed model improved the Mean Average Precision (mAP) performance by 0.78%–16.5% compared to stand-alone models and presents better mean average precision than other state-of-the-art methods (3.55% higher on the NWPU-VHR-10 dataset and 1.49% higher when using the RSOD dataset).

scholarly journals Jitter Detection Method Based on Sequence CMOS Images Captured by Rolling Shutter Mode for High-Resolution Remote Sensing Satellite

2022 ◽  
Vol 14 (2) ◽  
pp. 342
Author(s):  
Ying Zhu ◽  
Tingting Yang ◽  
Mi Wang ◽  
Hanyu Hong ◽  
Yaozong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Detection Method ◽  
Remote Sensing Data ◽  
Error Modeling ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Satellite Platform ◽  
Dense Matching ◽  
Remote Sensing Satellite

Satellite platform jitter is a non-negligible factor that affects the image quality of optical cameras. Considering the limitations of traditional platform jitter detection methods that are based on attitude sensors and remote sensing images, this paper proposed a jitter detection method using sequence CMOS images captured by rolling shutter for high-resolution remote sensing satellite. Through the three main steps of dense matching, relative jitter error analysis, and absolute jitter error modeling using sequence CMOS images, the periodic jitter error on the imaging focal plane of the spaceborne camera was able to be measured accurately. The experiments using three datasets with different jitter frequencies simulated from real remote sensing data were conducted. The experimental results showed that the jitter detection method using sequence CMOS images proposed in this paper can accurately recover the frequency, amplitude, and initial phase information of satellite jitter at 100 Hz, 10 Hz, and 2 Hz. Additionally, the detection accuracy reached 0.02 pixels, which can provide a reliable data basis for remote sensing image jitter error compensation.

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