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Convolutional Neural Networks (CNNs) are models that are utilized extensivelyfor the hierarchical extraction of features. Vision transformers (ViTs),through the use of a self-attention mechanism, have recently achieved superiormodeling of global contextual information compared to CNNs. However, to realizetheir image classification strength, ViTs require substantial trainingdatasets. Where the available training data are limited, current advancedmulti-layer perceptrons (MLPs) can provide viable alternatives to both deepCNNs and ViTs. In this paper, we developed the SGU-MLP, a learning algorithmthat effectively uses both MLPs and spatial gating units (SGUs) for preciseland use land cover (LULC) mapping. Results illustrated the superiority of thedeveloped SGU-MLP classification algorithm over several CNN and CNN-ViT-basedmodels, including HybridSN, ResNet, iFormer, EfficientFormer and CoAtNet. Theproposed SGU-MLP algorithm was tested through three experiments in Houston,USA, Berlin, Germany and Augsburg, Germany. The SGU-MLP classification modelwas found to consistently outperform the benchmark CNN and CNN-ViT-basedalgorithms. For example, for the Houston experiment, SGU-MLP significantlyoutperformed HybridSN, CoAtNet, Efficientformer, iFormer and ResNet byapproximately 15%, 19%, 20%, 21%, and 25%, respectively, in terms of averageaccuracy. The code will be made publicly available athttps://github.com/aj1365/SGUMLP

Spatial Gated Multi-Layer Perceptron for Land Use and Land Cover Mapping