A Method of Ship Detection under Complex BackgroundReportar como inadecuado


A Method of Ship Detection under Complex Background


A Method of Ship Detection under Complex Background - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

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

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





Autor: Ting Nie 1,2,* , Bin He 2, Guoling Bi 2, Yu Zhang 2 and Wensheng Wang 1,2

Fuente: http://mdpi.com/



DESCARGAR PDF




Documentos relacionados