Next Article in Journal
A Map Spectrum-Based Spatiotemporal Clustering Method for GDP Variation Pattern Analysis Using Nighttime Light Images of the Wuhan Urban Agglomeration
Previous Article in Journal
Registration of Multi-Level Property Rights in 3D in The Netherlands: Two Cases and Next Steps in Further Implementation
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(6), 159; doi:10.3390/ijgi6060159

A Method of Ship Detection under Complex Background

1
University of Chinese Academy of Sciences, Beijing 100049, China
2
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Dongnanhu Street, Changchun 130033, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 30 March 2017 / Revised: 18 May 2017 / Accepted: 28 May 2017 / Published: 31 May 2017

Abstract

The detection of ships in optical remote sensing images with clouds, waves, and other complex interferences is a challenging task with broad applications. Two main obstacles for ship target detection are how to extract candidates in a complex background, and how to confirm targets in the event that targets are similar to false alarms. In this paper, we propose an algorithm based on extended wavelet transform and phase saliency map (PSMEWT) to solve these issues. First, multi-spectral data fusion was utilized to separate the sea and land areas, and the morphological method was used to remove isolated holes. Second, extended wavelet transform (EWT) and phase saliency map were combined to solve the problem of extracting regions of interest (ROIs) from a complex background. The sea area was passed through the low-pass and high-pass filter to obtain three transformed coefficients, and the adjacent high frequency sub-bands were multiplied for the final result of the EWT. The visual phase saliency map of the product was built, and locations of ROIs were obtained by dynamic threshold segmentation. Contours of the ROIs were extracted by texture segmentation. Morphological, geometric, and 10-dimensional texture features of ROIs were extracted for target confirmation. Support vector machine (SVM) was used to judge whether targets were true. Experiments showed that our algorithm was insensitive to complex sea interferences and very robust compared with other state-of-the-art methods, and the recall rate of our algorithm was better than 90%. View Full-Text
Keywords: ship detection; extended wavelet transform; phase saliency map; multiple features analysis; SVM classifier; complex sea conditions ship detection; extended wavelet transform; phase saliency map; multiple features analysis; SVM classifier; complex sea conditions
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Nie, T.; He, B.; Bi, G.; Zhang, Y.; Wang, W. A Method of Ship Detection under Complex Background. ISPRS Int. J. Geo-Inf. 2017, 6, 159.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top