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Open AccessArticle

Multi-Scale DenseNets-Based Aircraft Detection from Remote Sensing Images

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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School of Geosciences and Info-physics, Central South University, Changsha 410083, China
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China Satellite Navigation Office, Beijing 100034, China
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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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Shandong Aerospace Electronic Technology Institute, Yantai 264000, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5270; https://doi.org/10.3390/s19235270
Received: 22 October 2019 / Revised: 23 November 2019 / Accepted: 27 November 2019 / Published: 29 November 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Deep learning-based aircraft detection methods have been increasingly implemented in recent years. However, due to the multi-resolution imaging modes, aircrafts in different images show very wide diversity on size, view and other visual features, which brings great challenges to detection. Although standard deep convolution neural networks (DCNN) can extract rich semantic features, they destroy the bottom-level location information. The features of small targets may also be submerged by redundant top-level features, resulting in poor detection. To address these problems, we proposed a compact multi-scale dense convolutional neural network (MS-DenseNet) for aircraft detection in remote sensing images. Herein, DenseNet was utilized for feature extraction, which enhances the propagation and reuse of the bottom-level high-resolution features. Subsequently, we combined feature pyramid network (FPN) with DenseNet to form a MS-DenseNet for learning multi-scale features, especially features of small objects. Finally, by compressing some of the unnecessary convolution layers of each dense block, we designed three new compact architectures: MS-DenseNet-41, MS-DenseNet-65, and MS-DenseNet-77. Comparative experiments showed that the compact MS-DenseNet-65 obtained a noticeable improvement in detecting small aircrafts and achieved state-of-the-art performance with a recall of 94% and an F1-score of 92.7% and cost less computational time. Furthermore, the experimental results on robustness of UCAS-AOD and RSOD datasets also indicate the good transferability of our method. View Full-Text
Keywords: remote sensing images; aircraft detection; compact multi-scale dense convolutional neural network; multi-scale training remote sensing images; aircraft detection; compact multi-scale dense convolutional neural network; multi-scale training
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MDPI and ACS Style

Wang, Y.; Li, H.; Jia, P.; Zhang, G.; Wang, T.; Hao, X. Multi-Scale DenseNets-Based Aircraft Detection from Remote Sensing Images. Sensors 2019, 19, 5270.

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