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

TL;DR

This paper introduces SGU-MLP, a novel model combining multi-layer perceptrons and spatial gating units, which outperforms CNNs and ViTs in land use and land cover mapping with limited training data.

Contribution

The paper presents the SGU-MLP model, a new approach that effectively integrates MLPs and spatial gating units for improved land cover classification, especially with limited data.

Findings

SGU-MLP outperforms CNN and ViT-based models in land cover mapping.

Significant accuracy improvements over benchmarks in three different cities.

Model demonstrates consistent robustness across diverse geographic datasets.

Abstract

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