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- Title
Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images.
- Authors
Wang, Eric Ke; Li, Yueping; Nie, Zhe; Yu, Juntao; Liang, Zuodong; Zhang, Xun; Yiu, Siu Ming
- Abstract
With the rapid growth of high-resolution remote sensing image-based applications, one of the fundamental problems in managing the increasing number of remote sensing images is automatic object detection. In this paper, we present a fusion feature-based deep learning approach to detect objects in high-resolution remote sensing images. It employs fine-tuning from ImageNet as a pre-training model to address the challenge of it lacking a large amount of training datasets in remote sensing. Besides, we improve the binarized normed gradients algorithm by multiple weak feature scoring models for candidate window selection and design a deep fusion feature extraction method with the context feature and object feature. Experiments are performed on different sizes of high-resolution optical remote sensing images. The results show that our model is better than regular models, and the average detection accuracy is 8.86% higher than objNet.
- Subjects
OPTICAL remote sensing; OPTICAL resolution; REMOTE sensing; DEEP learning; FEATURE extraction; WINDOW design &; construction
- Publication
Applied Sciences (2076-3417), 2019, Vol 9, Issue 6, p1130
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app9061130