点击次数：
影响因子：5.349
DOI码：10.3390/rs11091089
发表刊物：Remote Sensing
摘要：Traditional oil tank detection methods often use geometric shape information. However, it is difficult to guarantee accurate detection under a variety of disturbance factors, especially various colors, scale differences, and the shadows caused by view angle and illumination. Therefore, we propose an unsupervised saliency model with Color Markov Chain (US-CMC) to deal with oil tank detection. To avoid the influence of shadows, we make use of the CIE Lab space to construct a Color Markov Chain and generate a bottom-up latent saliency map. Moreover, we build a circular feature map based on a radial symmetric circle, which makes true targets to be strengthened for a subjective detection task. Besides, we combine the latent saliency map with the circular feature map, which can effectively suppress other salient regions except for oil tanks. Extensive experimental results demonstrate that it outperforms 15 saliency models for remote sensing images (RSIs). Compared with conventional oil tank detection methods, US-CMC has achieved better results and is also more robust for view angle, shadow, and shape similarity problems.
论文类型：期刊论文
一级学科：控制科学与工程
文献类型：期刊
卷号：11
期号：9
是否译文：否
发表时间：2019-05-07
收录刊物：SCI
发布期刊链接：https://www.mdpi.com/2072-4292/11/9/1089