A Superpixel-based Water Scene Segmentation Method by Sea-sky-line and Shoreline Detection

• Published in: 2021 International Conference on Security, Pattern Analysis, and Cybernetics（SPAC pp. 413 - 418
• Main Authors: Zhou, Zhiguo, Liu, Shiqi, Duan, Junwei, Aikaterini, Melliou
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.06.2021
• Subjects: Image segmentation; Interference; Reflection; Sea surface; sea-sky-line; shoreline; superpixel; Surface morphology; Surface reconstruction; Unmanned aerial vehicles; USV
• DOI: 10.1109/SPAC53836.2021.9539957

For the unmanned surface vehicle (USV) autonomous navigation and obstacle avoidance in the inland waters and coastal areas, it's essential to understand the water scene and divide the navigable area by taking the sea-sky-line and shoreline as the reference. Owing to the noise interference such as water surface reflection, it is not easy to detect the sea-sky-line and shoreline. In order to segment the water scene more accurately, the gradient image is firstly generated by Sobel operator, and then the contour of sea-sky-line and shoreline is enhanced by superposition with the image eliminated by sea surface reflection. Considering the local features in each image partition, superpixels are generated by multi-scale morphological gradient reconstruction (MMGR) and watershed algorithm. Finally, the superpixels is aggregated by fuzzy c-means (FCM), to get water scene segmentation. Experimental results show that the algorithm proposed (SEFCM) is superior to other similar algorithms in the accuracy of water scene segmentation, and more robust to illumination interference.