30 November 2017 Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jingbo Chen, Chengyi Wang, Anzhi Yue, Jiansheng Chen, Dongxu He, Xiuyan Zhang, "Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data," Journal of Applied Remote Sensing 11(4), 042619 (30 November 2017). https://doi.org/10.1117/1.JRS.11.042619 . Submission: Received: 16 May 2017; Accepted: 10 November 2017
Received: 16 May 2017; Accepted: 10 November 2017; Published: 30 November 2017
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