Abstract: [Purposes]To address the issues of low accuracy and adaptability with current remote sensing image coastline extraction methods.[Methods]This paper proposes a new coastline extraction method that combines adaptive simple linear iterative clustering (SLIC) and fractal net evolution approach (FNEA),providing dependable data support for precise coastal zone monitoring and protection.Firstly,according to the complexity of the image,the optimal number of superpixel segmentation blocks was calculated,and the SLIC theory algorithm was used to realize the adaptive superpixel segmentation of remote sensing images.Then,the regional similarity between superpixels is defined according to multiple features,and FNEA is used to merge superpixels to achieve coastline extraction.[Findings]In order to verify the superiority of the proposed algorithm,the proposed method and four comparison methods are used to conduct experiments on coastline remote sensing images with different complexities.The average recall rates are 96.46%,85.34%,89.54%,92.29%and 92.15%,respectively,and the average Kappa coefficients are 0.93,0.63,0.80,0.81 and 0.86,respectively.[Conclusions]The proposed method achieves both adaptive superpixel segmentation and precise coastline extraction.

Key words: coastline extraction, adaptive superpixel segmentation, image complexity, FNEA