AE-SAM: An Adaptive Enhanced Segment Anything Model Framework for Automated Land Cover Classification in Natural and Semi-Natural Environments

Land cover classification in high-resolution remote sensing imagery of natural and semi-natural scenes typically requires extensive manual annotation. Although the Segment Anything Model (SAM) exhibits powerful zero-shot segmentation capability, its class-agnostic nature and heavy reliance on manual prompts hinder fully automated semantic classification in complex natural scenes. This study introduces an adaptive enhanced Segment Anything Model framework (AESAM) that achieves fully automated land cover classification using only noisy, low-resolution global land cover products as priors. The framework incorporates a noise-resilient semantic alignment mechanism that reconstructs high-fidelity point prompts from low-resolution global land cover products through adaptive covariance-guided outlier filtering, multi-modal feature clustering, and spatial homogeneity constraints. These optimized prompts drive SAM to produce multiple hypothesis masks that are subsequently fused into a coherent classification map via adaptive optimization of model confidence and spatial consistency. Validated on 15 data sets with various resolutions (3 to 60 cm), AE-SAM achieved an average overall accuracy (OA) of 96.52%, a Kappa coefficient of 0.907, and a mean intersection over union (mIoU) of 0.855. Notably, our method outperforms the zero-shot SAM Automatic Mask Generator by 7.79 percentage points in OA, 0.162 in Kappa coefficient, and 0.138 in mIoU. It also surpasses few-shot segmentation approaches such as RSAM-Seg and traditional classifiers like Random Forest when all methods are evaluated under the few-shot setting. The results demonstrate that AE-SAM enables fully automated, high-accuracy land cover classification in natural and semi-natural environments using only coarse, noisy global priors–offering a practical solution for large-scale mapping in which manual annotation is infeasible. The source code is publicly available at https://github.com/sophie1987/AE-SAM/blob/main/sam_update.py