Fast Ship Detection in Remote Sensing Images Based on Multi-Attention Mechanism

At present, reliable and precise ship detection in high-resolution optical remote sensing images affected by wave clutter, thin clouds, and islands under complex sea conditions is still challenging. At the same time, object detection algorithms in satellite remote sensing images are challenged by color, aspect ratio, complex background, and angle variability. Even the results obtained based on the latest convolutional neural network (CNN) method are not satisfactory. In order to obtain more accurate ship detection results, this paper proposes a remote sensing image ship object detection method based on a brainlike visual attention mechanism. We refer to the robust expression mode of the human brain, design a vector field filter with active rotation capability, and explicitly encode the direction information of the remote sensing object in the neural network. The progressive enhancement learning model guided by the visual attention mechanism is used to dynamically solve the problem, and the object can be discovered and detected through time–space information. To verify the effectiveness of the proposed method, a remote sensing ship object detection data set is established, and the proposed method is compared with other state-of-the-art methods on the established data set. Experiments show that the object detection accuracy of this method and the ability to capture image details have been improved. Compared with other models, the average intersection rate of the joint is 80.12%, which shows a clear advantage. The proposed method is fast enough to meet the needs of ship detection in remote sensing images.

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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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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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Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

Remote Sensing ◽

10.3390/rs13091713 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1713

Author(s):

Songwei Gu ◽

Rui Zhang ◽

Hongxia Luo ◽

Mengyao Li ◽

Huamei Feng ◽

...

Keyword(s):

Remote Sensing ◽

Real Life ◽

Attention Mechanism ◽

Training Data ◽

Generative Adversarial Networks ◽

Natural Image ◽

Remote Sensing Images ◽

Training Time ◽

Adversarial Networks ◽

Remote Sensing Image Classification

Deep learning is an important research method in the remote sensing field. However, samples of remote sensing images are relatively few in real life, and those with markers are scarce. Many neural networks represented by Generative Adversarial Networks (GANs) can learn from real samples to generate pseudosamples, rather than traditional methods that often require more time and man-power to obtain samples. However, the generated pseudosamples often have poor realism and cannot be reliably used as the basis for various analyses and applications in the field of remote sensing. To address the abovementioned problems, a pseudolabeled sample generation method is proposed in this work and applied to scene classification of remote sensing images. The improved unconditional generative model that can be learned from a single natural image (Improved SinGAN) with an attention mechanism can effectively generate enough pseudolabeled samples from a single remote sensing scene image sample. Pseudosamples generated by the improved SinGAN model have stronger realism and relatively less training time, and the extracted features are easily recognized in the classification network. The improved SinGAN can better identify sub-jects from images with complex ground scenes compared with the original network. This mechanism solves the problem of geographic errors of generated pseudosamples. This study incorporated the generated pseudosamples into training data for the classification experiment. The result showed that the SinGAN model with the integration of the attention mechanism can better guarantee feature extraction of the training data. Thus, the quality of the generated samples is improved and the classification accuracy and stability of the classification network are also enhanced.

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Semantic Segmentation of High Resolution Remote Sensing Images with Extra Context Attention Mechanism

2020 IEEE 20th International Conference on Communication Technology (ICCT) ◽

10.1109/icct50939.2020.9295814 ◽

2020 ◽

Author(s):

Weifu Fu ◽

Qing Peng ◽

Yanxiang Gong ◽

Mei Xie ◽

Shicheng Wang ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Semantic Segmentation ◽

Attention Mechanism ◽

Remote Sensing Images

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Remote Sensing Road Extraction by Road Segmentation Network

Applied Sciences ◽

10.3390/app11115050 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5050

Author(s):

Jiahai Tan ◽

Ming Gao ◽

Kai Yang ◽

Tao Duan

Keyword(s):

Remote Sensing ◽

Attention Mechanism ◽

Context Information ◽

Road Extraction ◽

Remote Sensing Images ◽

Long Distance ◽

The Road ◽

Road Segmentation ◽

Context Characteristics

Road extraction from remote sensing images has attracted much attention in geospatial applications. However, the existing methods do not accurately identify the connectivity of the road. The identification of the road pixels may be interfered with by the abundant ground such as buildings, trees, and shadows. The objective of this paper is to enhance context and strip features of the road by designing UNet-like architecture. The overall method first enhances the context characteristics in the segmentation step and then maintains the stripe characteristics in a refinement step. The segmentation step exploits an attention mechanism to enhance the context information between the adjacent layers. To obtain the strip features of the road, the refinement step introduces the strip pooling in a refinement network to restore the long distance dependent information of the road. Extensive comparative experiments demonstrate that the proposed method outperforms other methods, achieving an overall accuracy of 98.25% on the DeepGlobe dataset, and 97.68% on the Massachusetts dataset.

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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

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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.

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