Paper Info
Title
A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network.
Abstract
Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control. Most ship detection algorithms utilize a single-band image to design low-level and hand-crafted features, which are easily influenced by interference like clouds and strong waves and not robust for large-scale variation of ships. In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems. First, multi-spectral images are adopted for sea-land segmentation, and an enhanced DWT is employed to quickly extract ship candidate regions with missing alarms as low as possible. Second, panchromatic images with clear spatial details are used for ship classification. Specifically, we propose the local residual dense block (LRDB) to fully extract semantic feature via local residual connection and densely connected convolutional layers. DRDN mainly consists of four LRDBs and is designed to further remove false alarms. Furthermore, we exploit the multiclass classification strategy, which can overcome the large intra-class difference of targets and identify ships of different sizes. Extensive experiments demonstrate that the proposed method has high robustness in complex image backgrounds and achieves higher detection accuracy than other state-of-the-art methods.
Year
DOI
Venue
2020
10.3390/rs12193115
REMOTE SENSING
Keywords
DocType
Volume
ship detection,optical remote sensing,discrete wavelet transform,deep residual dense network,multiclass classification
Journal
12
Issue
Citations 
PageRank 
19
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Liqiong Chen17519.61
Wen-Xuan Shi2124.20
Cien Fan313.06
Lian Zou412.38
Dexiang Deng5694.43