A method of ship detection under complex background

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%.