An Improved Simulated Annealing Sampling Method for Land-Cover Accuracy Assessment

Landscape heterogeneity is the core attribute of land cover landscape patterns, and a critical factor that must be considered in land cover accuracy assessment. This paper regards sampling in land cover accuracy assessment as essentially a multi-objective optimization problem, where uniform distribution avoids spatial autocorrelation while accounting for landscape heterogeneity to enhance the robustness of evaluation results. This paper proposes a land cover accuracy assessment method based on landscape index-improved simulated annealing sampling. By adopting a perturbation function that considers spatial equilibrium, it uses the Aggregation Index (AI) and Landscape Shape Index (LSI) to modify the objective function, optimizing the sample uniformity in low-heterogeneity areas and increasing the sample coverage in high-heterogeneity regions to achieve dual sampling optimization. Taking Jiangxi Province as the study area, the performance of the proposed method is verified through two experiments. In the first experiment, accuracy assessment is carried out on three sets of 30 m land cover products, namely GlobeLand30, CLCD and GLC_FCS. The two sample sets generated by LSI optimization and AI optimization yield highly consistent results in terms of User’s Accuracy (UA), Producer’s Accuracy (PA), area-weighted overall accuracy and Kappa coefficient, which demonstrates that the proposed method has stable cross-product generality. The second comparative experiment with four types of traditional sampling methods shows that the traditional methods cannot complete the accuracy assessment of three rare land categories including wetland, shrubland and bare land, and supplementary samples are required through secondary sampling. The optimized method can complete the PA and UA assessment of all land categories in a single run, while ensuring that the global overall accuracy and Kappa coefficient are consistent with those of traditional methods.