Paper Info
Title
Automatic Ship Detection in Optical Remote Sensing Images Based on Anomaly Detection and SPP-PCANet.
Abstract
Automatic ship detection technology in optical remote sensing images has a wide range of applications in civilian and military fields. Among most important challenges encountered in ship detection, we focus on the following three selected ones: (a) ships with low contrast; (b) sea surface in complex situations; and (c) false alarm interference such as clouds and reefs. To overcome these challenges, this paper proposes coarse-to-fine ship detection strategies based on anomaly detection and spatial pyramid pooling pcanet (SPP-PCANet). The anomaly detection algorithm, based on the multivariate Gaussian distribution, regards a ship as an abnormal marine area, effectively extracting candidate regions of ships. Subsequently, we combine PCANet and spatial pyramid pooling to reduce the amount of false positives and improve the detection rate. Furthermore, the non-maximum suppression strategy is adopted to eliminate the overlapped frames on the same ship. To validate the effectiveness of the proposed method, GF-1 images and GF-2 images were utilized in the experiment, including the three scenarios mentioned above. Extensive experiments demonstrate that our method obtains superior performance in the case of complex sea background, and has a certain degree of robustness to external factors such as uneven illumination and low contrast on the GF-1 and GF-2 satellite image data.
Year
DOI
Venue
2019
10.3390/rs11010047
REMOTE SENSING
Keywords
Field
DocType
ship detection,anomaly detection,multivariate Gaussian,spatial pyramid pooling pcanet
Anomaly detection,Computer vision,Remote sensing,Artificial intelligence,Geology
Journal
Volume
Issue
Citations 
11
1
1
PageRank 
References 
Authors
0.35
22
4
Name
Order
Citations
PageRank
Nan Wang19327.47
Baochun Li29416614.20
Qizhi Xu3919.24
Yonghua Wang45311.75