Coastal tourist attractions remote sensing image information extraction and model establishment based on IT2FCM algorithm

Objective The spatial distribution and dynamic changes of water bodies, vegetation, beaches, and buildings in coastal tourist attractions directly affect regional tourism planning, ecological protection, and disaster early warning. However, in remote sensing images of these areas, the spectral overlap of ground features is severe and the boundaries are blurred. Coupled with the continuous interference of tides and fog, existing methods struggle to efficiently and accurately acquire the spatial information of these key elements. This study aims to construct an information extraction model that can effectively address these problems. Methods Interval type II fuzzy C-means clustering is used as the core segmentation algorithm. Particle swarm optimization and the Scherpennis index are used to adaptively adjust the clustering parameters. Gray-level co-occurrence matrix is introduced to extract texture features, and Fourier descriptors are used to extract shape features. Finally, the two types of features are fused and input into a classifier to complete ground feature identification. Results The accuracy of the core segmentation algorithm reaches 95.34%. Under a noise level of 10, the model achieved an average cross-union ratio of 98.10% for the water body, a beach boundary positioning error of 2.24 pixels, and shape similarity indices of 0.952, 0.904, 0.868, and 0.936 for the water body, vegetation, beach, and buildings, respectively. Conclusion The model proposed in this study outperforms existing comparative models in terms of segmentation accuracy, noise resistance, and geometric feature preservation, providing effective technical support for the dynamic monitoring and intelligent management of coastal tourism resources.