Ship Detection from Remote Sensing Images Based on Deep Learning

Micro-nano satellites have provided a large amount of remote sensing images for many earth observation applications. However, the hysteresis of satellite-ground mutual communication of massive remote sensing images and the low efficiency of traditional information processing flow have become the bottlenecks for the further development of micro-nano satellites. To solve this problem, this paper proposes an on-board ship detection scheme based on deep learning and Commercial Off-The-Shelf (COTS) component, which can be used to achieve near real-time on-board processing by micro-nano satellite computing platform. The on-board ship detection algorithm based on deep learning consists of a feature extraction network, Region Proposal Network (RPN) with square anchors, Global Average Pooling (GAP), and Bigger-Left Non-Maximum Suppression (BL-NMS). With the help of high performance COTS components, the proposed scheme can extract target patches and valuable information from remote sensing images quickly and accurately. A ground demonstration and verification system is built to verify the feasibility and effectiveness of our scheme. Our method achieves the performance with 95.9% recall and 80.5% precision in our dataset. Experimental results show that the scheme has a good application prospect in micro-nano satellites with limited power and computing resources.

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An Approach on Image Processing of Deep Learning Based on Improved SSD

Symmetry ◽

10.3390/sym13030495 ◽

2021 ◽

Vol 13 (3) ◽

pp. 495

Author(s):

Liang Jin ◽

Guodong Liu

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Remote Sensing Image ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Image Detection ◽

Ship Detection ◽

Real Time Detection

Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.

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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.

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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

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Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

Mobile Networks and Applications ◽

10.1007/s11036-020-01703-3 ◽

2021 ◽

Vol 26 (1) ◽

pp. 200-215

Author(s):

Muhammad Alam ◽

Jian-Feng Wang ◽

Cong Guangpei ◽

LV Yunrong ◽

Yuanfang Chen

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Semantic Segmentation ◽

Natural Scene ◽

Remote Sensing Images ◽

Advantages And Disadvantages ◽

Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

Remote Sensing ◽

10.3390/rs13132524 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2524

Author(s):

Ziyi Chen ◽

Dilong Li ◽

Wentao Fan ◽

Haiyan Guan ◽

Cheng Wang ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Semantic Segmentation ◽

Extraction Methods ◽

The Self ◽

Optical Remote Sensing ◽

Building Extraction ◽

Learning Models ◽

Remote Sensing Images ◽

Segmentation Methods

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

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CPS-Det: An Anchor-Free Based Rotation Detector for Ship Detection

Remote Sensing ◽

10.3390/rs13112208 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2208

Author(s):

Yi Yang ◽

Zongxu Pan ◽

Yuxin Hu ◽

Chibiao Ding

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Positive Sample ◽

Feature Points ◽

Annotation Scheme ◽

Ship Detection ◽

Learning Network ◽

Public Dataset ◽

Deep Learning Network ◽

Speed And Accuracy

Ship detection is a significant and challenging task in remote sensing. At present, due to the faster speed and higher accuracy, the deep learning method has been widely applied in the field of ship detection. In ship detection, targets usually have the characteristics of arbitrary-oriented property and large aspect ratio. In order to take full advantage of these features to improve speed and accuracy on the base of deep learning methods, this article proposes an anchor-free method, which is referred as CPS-Det, on ship detection using rotatable bounding box. The main improvements of CPS-Det as well as the contributions of this article are as follows. First, an anchor-free based deep learning network was used to improve speed with fewer parameters. Second, an annotation method of oblique rectangular frame is proposed, which solves the problem that periodic angle and bounded coordinates in conjunction with the regression calculation can lead to the problem of loss anomalies. For the annotation scheme proposed in this paper, a scheme for calculating Angle Loss is proposed, which makes the loss function of angle near the boundary value more accurate and greatly improves the accuracy of angle prediction. Third, the centerness calculation of feature points is optimized in this article so that the center weight distribution of each point is suitable for the rotation detection. Finally, a scheme combining centerness and positive sample screening is proposed and its effectiveness in ship detection is proved. Experiments on remote sensing public dataset HRSC2016 show the effectiveness of our approach.

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