Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data
Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data
复制标题
使用对时间增强数据进行微调的卷积神经网络进行知识引导的高尔夫球场检测
DOI：
10.1117/1.jrs.11.042619
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发表时间：
2017-11
影响因子：
1.7
通讯作者：
Zhang Xiuyan
中科院分区：
文献类型：
--
作者：
Chen Jingbo;Wang Chengyi;Yue Anzhi;Chen Jiansheng;He Dongxu;Zhang Xiuyan
抽象的。卷积神经网络等深度学习模型在计算机视觉领域的巨大成功为遥感领域的类似问题提供了一种方法。尽管将预先训练好的CNN重新用于遥感任务的研究正在兴起，但标记样本的稀缺性和遥感图像的复杂性仍然构成了挑战。我们开发了一种知识引导的高尔夫球场检测方法，使用CNN对时间增强的数据进行微调。该方法结合了知识驱动的区域建议、基于CNN的数据驱动检测和知识驱动的后处理。为了应对数据的复杂性，依次应用基于知识的共现规则、合成规则和基于区域的规则来推荐候选高尔夫球场。为了应对样本稀缺的问题，我们在时间域采用了数据增强的方法，从多时相图像中提取样本。然后，扩大的样本被用来微调预先训练的CNN以进行高尔夫检测。最后，通过后处理进一步抑制了佣金误差。在GF-1图像上进行的实验证明了该方法的有效性。
Abstract. The tremendous success of deep learning models such as convolutional neural networks (CNNs) in computer vision provides a method for similar problems in the field of remote sensing. Although research on repurposing pretrained CNN to remote sensing tasks is emerging, the scarcity of labeled samples and the complexity of remote sensing imagery still pose challenges. We developed a knowledge-guided golf course detection approach using a CNN fine-tuned on temporally augmented data. The proposed approach is a combination of knowledge-driven region proposal, data-driven detection based on CNN, and knowledge-driven postprocessing. To confront data complexity, knowledge-derived cooccurrence, composition, and area-based rules are applied sequentially to propose candidate golf regions. To confront sample scarcity, we employed data augmentation in the temporal domain, which extracts samples from multitemporal images. The augmented samples were then used to fine-tune a pretrained CNN for golf detection. Finally, commission error was further suppressed by postprocessing. Experiments conducted on GF-1 imagery prove the effectiveness of the proposed approach.
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影响因子：
5
作者：
Zhou, Weixun;Newsam, Shawn;Shao, Zhenfeng
通讯作者：
Shao, Zhenfeng
DOI：
10.1109/icecc.2011.6067611
发表时间：
2011-11
期刊：
2011 International Conference on Electronics, Communications and Control (ICECC)
影响因子：
--
作者：
C.-I Chen;Jianfei Chen;Xiaolin Zhang
通讯作者：
C.-I Chen;Jianfei Chen;Xiaolin Zhang
影响因子：
4.8
作者：
Luus, F. P. S.;Salmon, B. P.;Maharaj, B. T. J.
通讯作者：
Maharaj, B. T. J.
DOI：
10.1109/avss.2015.7301739
发表时间：
2015-10
期刊：
2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
影响因子：
--
作者：
Niall McLaughlin;J. M. D. Rincón;P. Miller
通讯作者：
Niall McLaughlin;J. M. D. Rincón;P. Miller
影响因子：
3.4
作者：
Esam Othman;Y. Bazi;N. Alajlan;H. Alhichri;F. Melgani
通讯作者：
Esam Othman;Y. Bazi;N. Alajlan;H. Alhichri;F. Melgani