Foreground Refinement Network for Rotated Object Detection in Remote Sensing Images

Zhang, Tianyang; Zhang, Xiangrong; Zhu, Peng; Chen, Puhua; Tang, Xu; Li, Chen; Jiao, Licheng

doi:10.1109/tgrs.2021.3109145

Cited by 28 publications

(15 citation statements)

References 60 publications

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“…To address the challenge of complex background in remote sensing image target detection, Zhang et al [207] proposed a foreground-aware remote sensing image target detection model, which enhanced the foreground-awareness of the detector from the perspectives of feature relationship learning and network optimization. The method enhanced the discriminative ability of foreground regions in feature maps by building a foreground relation learning module and introducing a foreground anchor loss function to enable the network to focus on the optimization of foreground anchors.…”

Section: Multi-oriented Object Representationmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

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

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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Section: Multi-oriented Object Representationmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

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

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“…3) Experimental Results: Quantitative Results and Analyses: Table VIII-IX show the results of OBB detection experiments. On the challenging DOTA dataset, it can be seen that using the advanced ORCN framework, the models 2 https://github.com/jbwang1997/OBBDetection IMP-VGG-16 79.6 RRD [125] IMP-VGG-16 84.3 FoRDet [126] IMP-VGG-16 89.9 R2CNN [127] IMP-ResNet-101 73.1 Rotated RPN [128] IMP-ResNet-101 79.1 ROI Transformer [117] IMP-ResNet-101-FPN 86.2 Gliding Vertex [119] IMP-ResNet-101-FPN 88.2 GRS-Det [129] IMP-ResNet-50-FPN 88.9 GRS-Det [129] IMP-ResNet- whose backbone is either ResNet-50 or Swin-T performs well, although the mAPs of Swin-T models are slightly lower than the ResNet models. The ViTAEv2-S, which is a kind of vision transformer network that is introduced the inductive biases including the locality and scale-invariance characteristics of CNN, obtains amazing performance that improve the ORCN baseline by nearly 2% mAP.…”

Section: Aerial Object Detectionmentioning

confidence: 99%

An Empirical Study of Remote Sensing Pretraining

Wang¹,

Zhang²,

Du³

et al. 2022

Preprint

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“…This paper mainly focuses on anchor-based detectors. Anchor-based detectors can be divided into multi-stage detectors [23][24][25][26][27][28][29][30][31] and one-stage detectors [32][33][34][35][36][37].…”

Section: A Object Detection In Remote Sensing Imagerymentioning

confidence: 99%

Context-Based Oriented Object Detector for Small Objects in Remote Sensing Imagery

et al. 2022

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Object detection in remote sensing imagery is a challenging task in the field of computer vision and has high research value. To improve the classification accuracy and positioning accuracy of object detection, we propose a new multi-scale oriented object detector suitable for small objects. Firstly, the feature fusion network based on information balance (IBFF) is proposed to reduce the reuse of different layers' features from the backbone network and reduce the interference of redundant information based on the premise that the output features have sufficient information, and retain enough shallow detail information. Secondly, to efficiently utilize deep and shallow features, enhance important features, and reduce background noise interference, different attention-based context feature fusion modules (DACFF) are designed according to the characteristics of different feature fusion stages. Finally, an improved strategy of oriented bounding box regression is proposed to obtain the oriented bounding box with a simpler and more effective strategy. The proposed method was evaluated on two public remote sensing datasets, DOTA and HRSC2016, and their mAP values are 80.96% and 95.01%, respectively, which verified the effectiveness of the proposed algorithm.

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Foreground Refinement Network for Rotated Object Detection in Remote Sensing Images

Cited by 28 publications

References 60 publications

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

An Empirical Study of Remote Sensing Pretraining

Context-Based Oriented Object Detector for Small Objects in Remote Sensing Imagery

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