A High-Precision Land-Sea Segmentation Model Based on the Deep Otsu Method

Abstract. Land-sea segmentation is crucial for tasks such as marine target detection and coastline extraction in remote sensing imagery. However, complex and diverse background environments and land-sea boundaries can easily lead to inaccurate segmentation. To address this issue, a high-precision land-sea segmentation model based on the deep Otsu method is proposed. This method first utilizes our proposed remote sensing image texture enhancement algorithm based on Retinex theory and the Canny operator to enhance the remote sensing image and its edge information, further improving the segmentation accuracy of the land-sea boundary. Then, we combine deep learning concepts, the maximum inter-class variance method, and our proposed density space clustering method based on the difference innovation optimization algorithm to propose a deep maximum inter-class variance method for segmenting the ocean and land in the image. Simultaneously, an adaptive multi-scale fragmentation region removal method is proposed to remove small, fragmented regions extracted during the segmentation process. Experimental results show that the proposed method achieves an overall prediction accuracy of 98.41% and an average intersection-union ratio of 96.07%, demonstrating its ability to effectively perform land-sea segmentation tasks.