Multiple Features Based Low-Contrast Infrared Ship Image Segmentation Using Fuzzy Inference System

Abstract

Infrared (IR) ship image segmentation is a challenging task due to defects of IR images, such as low-contrast, sea clutters, noises and etc. Aiming to solve this problem, we propose a multiple features based IR ship image segmentation method using fuzzy inference system (FIS). Because of complexness of the low-contrast IR image, the ship target cannot be segmented by only one kind of feature. Thus we extract multiple features from IR image to sufficiently represent the ship target. As the FIS can well handle the uncertainty of IR image and express expert knowledge with fuzzy rules, multiple features are input to FIS, then the ship target can be simply extracted from the output of FIS. In this paper, the proposed method is implemented as follows. Firstly, intensity is chosen as the first input of FIS, because it is fundamental feature of ship target in IR image. Secondly, the spatial feature is constructed through saliency detection, region growing and morphology processing, which is used to represent spatial constrain of ship target region. Thirdly, the multiple features are fuzzified with adaptive methods and prior knowledge. Fourthly, the fuzzified features are well combined through FIS, according to the fuzzy rules based on expert knowledge. Finally, the intact ship target segmentation can be simply extracted through the output of the FIS. Experimental results show that our method can effectively extracts the complete and precise ship targets from the low-contrast IR ship images. Moreover, our method performs better than other existed segmentation methods.