Parcel-Based Rice Identification and Uncertainty Analysis Under Multiscale Consistency Constraints

The precise mapping of crop spatial distribution using remote sensing datasets is a fundamental task in precision agriculture, yet conventional methods based on pixels or segmented objects often neglect the boundary constraints from the cultivated areas and correlations between analysis units. In response, the proposed work is a novel attempt to address parcelwise crop classification with multi-level consistency constraints, generating robust features and consequently more precise results. Pixel-level constraints enforce spectral homogeneity within parcels extracted by the neural network during feature construction. Each parcel is then correlated with its closest neighbors considering comprehensively the spatial, environmental, and temporal similarities, providing consistency information at the parcel scale. Two model structures based primarily on the graph neural network and attention mechanism were proposed for crop identification. Results show that both constraints may yield significant benefits given their enhancement to feature stability and completeness, especially in small-sample cases. The neighborhood models achieved average accuracy improvements of 3.10%, 3.47%, 4.65%, 4.34%, and 15.78%, respectively, under varying training set proportions of 0.5, 0.4, 0.3, 0.2, and 0.1, and an OA of 89.50% could still be maintained for the graph-based one even when the training set ratio was limited to 0.02. Within the calculation, various intelligent algorithms such as CNN, RF, GNN, and attention mechanisms are methodically leveraged for effective pattern recognition of both spatial, sequential, and neighborhood dimensions, achieving an efficacious uncertainty reduction. Overall, the proposed algorithm may serve as a universal framework for parcel-wise type inference, facilitating the effective implementation of precision agriculture.