FCC-Net: A Full-Coverage Collaborative Network for Weakly Supervised Remote Sensing Object Detection

With an ever-increasing resolution of optical remote-sensing images, how to extract information from these images efficiently and effectively has gradually become a challenging problem. As it is prohibitively expensive to label every object in these high-resolution images manually, there is only a s...

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Veröffentlicht in:	Electronics (Basel) Jg. 9; H. 9; S. 1356
Hauptverfasser:	Chen, Suting, Shao, Dongwei, Shu, Xiao, Zhang, Chuang, Wang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.09.2020
Schlagworte:	Ablation Accuracy Algorithms Boxes Classification Collaboration Datasets Detection Feature extraction Ground truth High resolution Image resolution Interdisciplinary subjects Labels Machine learning Neural networks Object recognition Remote sensing Scale invariance Semantics Sensors Training
ISSN:	2079-9292, 2079-9292
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Abstract	With an ever-increasing resolution of optical remote-sensing images, how to extract information from these images efficiently and effectively has gradually become a challenging problem. As it is prohibitively expensive to label every object in these high-resolution images manually, there is only a small number of high-resolution images with detailed object labels available, highly insufficient for common machine learning-based object detection algorithms. Another challenge is the huge range of object sizes: it is difficult to locate large objects, such as buildings and small objects, such as vehicles, simultaneously. To tackle these problems, we propose a novel neural network based remote sensing object detector called full-coverage collaborative network (FCC-Net). The detector employs various tailored designs, such as hybrid dilated convolutions and multi-level pooling, to enhance multiscale feature extraction and improve its robustness in dealing with objects of different sizes. Moreover, by utilizing asynchronous iterative training alternating between strongly supervised and weakly supervised detectors, the proposed method only requires image-level ground truth labels for training. To evaluate the approach, we compare it against a few state-of-the-art techniques on two large-scale remote-sensing image benchmark sets. The experimental results show that FCC-Net significantly outperforms other weakly supervised methods in detection accuracy. Through a comprehensive ablation study, we also demonstrate the efficacy of the proposed dilated convolutions and multi-level pooling in increasing the scale invariance of an object detector.
AbstractList	With an ever-increasing resolution of optical remote-sensing images, how to extract information from these images efficiently and effectively has gradually become a challenging problem. As it is prohibitively expensive to label every object in these high-resolution images manually, there is only a small number of high-resolution images with detailed object labels available, highly insufficient for common machine learning-based object detection algorithms. Another challenge is the huge range of object sizes: it is difficult to locate large objects, such as buildings and small objects, such as vehicles, simultaneously. To tackle these problems, we propose a novel neural network based remote sensing object detector called full-coverage collaborative network (FCC-Net). The detector employs various tailored designs, such as hybrid dilated convolutions and multi-level pooling, to enhance multiscale feature extraction and improve its robustness in dealing with objects of different sizes. Moreover, by utilizing asynchronous iterative training alternating between strongly supervised and weakly supervised detectors, the proposed method only requires image-level ground truth labels for training. To evaluate the approach, we compare it against a few state-of-the-art techniques on two large-scale remote-sensing image benchmark sets. The experimental results show that FCC-Net significantly outperforms other weakly supervised methods in detection accuracy. Through a comprehensive ablation study, we also demonstrate the efficacy of the proposed dilated convolutions and multi-level pooling in increasing the scale invariance of an object detector.
Author	Zhang, Chuang Wang, Jun Shu, Xiao Chen, Suting Shao, Dongwei
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SubjectTerms	Ablation Accuracy Algorithms Boxes Classification Collaboration Datasets Detection Feature extraction Ground truth High resolution Image resolution Interdisciplinary subjects Labels Machine learning Neural networks Object recognition Remote sensing Scale invariance Semantics Sensors Training
Title	FCC-Net: A Full-Coverage Collaborative Network for Weakly Supervised Remote Sensing Object Detection
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