Explicable Sub-Pixel Mapping Based on Nested Self-Attention Network with Spatial Correlation Learnable Mechanism

Convolutional-based sub-pixel mapping (SPM) is a cutting-edge approach to solve the mixed pixel problem in land cover mapping. However, the feature modeling process of convolutional neural networks (CNN) lacks interpretability, and thus, it is difficult to learn spatial correlation that is helpful for sub-pixel location reasoning, and usually fail in reconstruction of fragmented land parcels. Therefore, this study constructs a SPM network that combines data-driven and model-driven approaches (DEMON). It uses nested self-attention mechanisms to simulate spatial dependency modeling, which learns spatial correlations between sub-pixels and pixels of different land covers, the learned spatial correlations can then be used to explicitly infer the spatial positions to achieve interpretability. Three public datasets are used for validation, and we found that our proposed method outperforms SOTA methods by circa 6%, and the visualized spatial correlations verifies the interpretability of the model.