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LO-MLPRNN: A Classification Algorithm for Multispectral Remote Sensing Images by Fusing Selective Convolution
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DOI：10.3390/s25082472.png)
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Abstract
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To address the limitation of traditional deep learning algorithms in fully utilizing contextual information in multispectral remote sensing (RS) images, this paper proposes an improved vegetation cover classification algorithm called LO-MLPRNN, which integrates Large Selective Kernel Network (LSK) and Omni-Dimensional Dynamic Convolution (ODC) with a Multi-Layer Perceptron Recurrent Neural Network (MLPRNN). The algorithm employs parallel-connected ODC and LSK modules to adaptively adjust convolution kernel parameters across multiple dimensions and dynamically optimize spatial receptive fields, enabling multi-perspective feature fusion for efficient processing of multispectral band information. The extracted features are mapped to a high-dimensional space through a Gate Recurrent Unit (GRU) and fully connected layers, with nonlinear characteristics enhanced by activation functions, ultimately achieving pixel-level land cover classification. Experiments conducted on GF-2 (0.75 m) and Sentinel-2 (10 m) multispectral RS images from Liucheng County, Liuzhou City, Guangxi Province, demonstrate that LO-MLPRNN achieves overall accuracies of 99.11% and 99.43%, outperforming Vision Transformer (ViT) by 2.61% and 3.98%, respectively. Notably, the classification accuracy for sugarcane reaches 99.70% and 99.67%, showcasing its superior performance.
Keywords:
multispectral
remote sensing
multilayer perceptron
recurrent neural networks
Omni-Dimensional Dynamic Convolution
large selective convolutional networks
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