FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery

Sun, Xian; Wang, Peijin; Yan, Zhiyuan; Xu, Feng; Wang, Ruiping; Diao, Wenhui; Chen, Jin; Li, Jihao; Feng, Yingchao; Xu, Tao; Weinmann, Martin; Hinz, Stefan; Wang, Cheng; Fu, Kun

doi:10.1016/j.isprsjprs.2021.12.004

Cited by 229 publications

(88 citation statements)

References 53 publications

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“…We trained several networks on a new dataset FAIR1M [48] and tested the models. FAIR1M is a large dataset for fine-grained target detection and recognition in remote sensing images.…”

Section: F Tests On Dataset Fair1mmentioning

confidence: 99%

An Enhanced Lightweight Convolutional Neural Network for Ship Detection in Maritime Surveillance System

Yin

Cheng

Shi

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the extensive application of artificial intelligence, ship detection from optical satellite remote sensing images using deep learning technology can significantly improve detection accuracy. However, the existing methods usually have complex models and huge computations, which makes them difficult to deploy on resource-constrained devices such as satellites. To solve this problem, this paper proposes an enhanced lightweight ship detection model called ShipDetectionNet to replace the standard convolution with improved convolution units. The improved convolution unit is implemented by applying depthwise separable convolution to replace standard convolution and further using the pointwise group convolution to replace the point convolution in depthwise separable convolution. In addition, the Attention mechanism is incorporated into the convolution unit to ensure detection accuracy. Compared with the latest YOLOv5s, our model has a comparable performance in mean Average Precision (mAP), while the number of parameters and the model size are reduced by 14.18% and 13.14%, respectively. Compared to five different lightweight detection models, the proposed ShipDetectionNet is more competent for ship detection tasks. In addition, the ShipDetectionNet is evaluated on four challenging scenarios, demonstrating its generalizability and effectiveness.

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“…We trained several networks on a new dataset FAIR1M [48] and tested the models. FAIR1M is a large dataset for fine-grained target detection and recognition in remote sensing images.…”

Section: F Tests On Dataset Fair1mmentioning

confidence: 99%

An Enhanced Lightweight Convolutional Neural Network for Ship Detection in Maritime Surveillance System

Yin

Cheng

Shi

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…N OWADAYS object detection is essential in remote sensing (RS) imagery interpretation and also has a widespread application in natural resource management, intelligent agriculture, building detection, etc. In the past decade, with the development of deep learning algorithms [3,4], great progress has been made by deep convolutional neural networks (DCNN) in RS object detection [5][6][7][8][9][10][11][12][13]. However, the outstanding performance highly relies on large quantities of training data with annotations.…”

Section: Introductionmentioning

confidence: 99%

RFA-Net: Reconstructed Feature Alignment Network for Domain Adaptation Object Detection in Remote Sensing Imagery

Zhu

Sun

Diao

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, in high clutter rate and low detection probability scenarios, tracking accuracy of this method declines greatly. Feature-aided tracking methods which attempt to extract features from data for multi-target tracking have been used in video [ 21 ], satellite video [ 22 ], and high-resolution remote sensing imagery [ 23 ]. A feature-aided extended target probability hypothesis density filter for high resolution radar using the number and spatial extension of measurements as features to deal with extended target tracking was proposed by [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking

Tao

Jiang

Yang

et al. 2022

Sensors

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Multi-target tracking (MTT) is one of the most important functions of radar systems. Traditional multi-target tracking methods based on data association convert multi-target tracking problems into single-target tracking problems. When the number of targets is large, the amount of computation increases exponentially. The Gaussian mixture probability hypothesis density (GM-PHD) filtering based on a random finite set (RFS) provides an effective method to solve multi-target tracking problems without the requirement of explicit data association. However, it is difficult to track targets accurately in real-time with dense clutter and low detection probability. To solve this problem, this paper proposes a multi-feature matching GM-PHD (MFGM-PHD) filter for radar multi-target tracking. Using Doppler and amplitude information contained in radar echo to modify the weights of Gaussian components, the weight of the clutter can be greatly reduced and the target can be distinguished from clutter. Simulations show that the proposed MFGM-PHD filter can improve the accuracy of multi-target tracking as well as the real-time performance with high clutter density and low detection probability.

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FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery

Cited by 229 publications

References 53 publications

An Enhanced Lightweight Convolutional Neural Network for Ship Detection in Maritime Surveillance System

An Enhanced Lightweight Convolutional Neural Network for Ship Detection in Maritime Surveillance System

RFA-Net: Reconstructed Feature Alignment Network for Domain Adaptation Object Detection in Remote Sensing Imagery

Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking

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