Spatial Gated Multi-Layer Perceptron for Land Use and Land Cover Mapping
Ali Jamali Swalpa Kumar Roy Danfeng Hong Peter M Atkinson Pedram Ghamisi
Convolutional Neural Networks (CNNs) are models that are utilized extensively
for the hierarchical extraction of features. Vision transformers (ViTs),
through the use of a self-attention mechanism, have recently achieved superior
modeling of global contextual information compared to CNNs. However, to realize
their image classification strength, ViTs require substantial training
datasets. Where the available training data are limited, current advanced
multi-layer perceptrons (MLPs) can provide viable alternatives to both deep
CNNs and ViTs. In this paper, we developed the SGU-MLP, a learning algorithm
that effectively uses both MLPs and spatial gating units (SGUs) for precise
land use land cover (LULC) mapping. Results illustrated the superiority of the
developed SGU-MLP classification algorithm over several CNN and CNN-ViT-based
models, including HybridSN, ResNet, iFormer, EfficientFormer and CoAtNet. The
proposed SGU-MLP algorithm was tested through three experiments in Houston,
USA, Berlin, Germany and Augsburg, Germany. The SGU-MLP classification model
was found to consistently outperform the benchmark CNN and CNN-ViT-based
algorithms. For example, for the Houston experiment, SGU-MLP significantly
outperformed HybridSN, CoAtNet, Efficientformer, iFormer and ResNet by
approximately 15%, 19%, 20%, 21%, and 25%, respectively, in terms of average
accuracy. The code will be made publicly available at
https://github.com/aj1365/SGUMLP