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LO-MLPRNN: A Classification Algorithm for Multispectral Remote Sensing Images by Fusing Selective Convolution.

Xiangsuo Fan^1,2,3, Yan Zhang¹, Yong Peng¹

¹School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.

Sensors (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

This study introduces LO-MLPRNN, an advanced deep learning model for vegetation cover classification in remote sensing images. It significantly improves accuracy by better utilizing contextual information, outperforming existing methods.

Keywords:

Omni-Dimensional Dynamic Convolution large selective convolutional networks multilayer perceptron multispectral recurrent neural networks remote sensing

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Area of Science:

Remote Sensing
Deep Learning
Computer Vision

Background:

Traditional deep learning methods struggle to fully leverage contextual information in multispectral remote sensing (RS) images.
Accurate land cover classification is crucial for environmental monitoring and resource management.

Purpose of the Study:

To propose an improved vegetation cover classification algorithm, LO-MLPRNN, that enhances the utilization of contextual information in multispectral RS images.
To achieve accurate pixel-level land cover classification by integrating advanced deep learning modules.

Main Methods:

The proposed LO-MLPRNN algorithm integrates Large Selective Kernel Network (LSK) and Omni-Dimensional Dynamic Convolution (ODC) with a Multi-Layer Perceptron Recurrent Neural Network (MLPRNN).
Parallel-connected ODC and LSK modules adaptively adjust convolution kernel parameters and optimize spatial receptive fields for multi-perspective feature fusion.
Extracted features are processed through a Gate Recurrent Unit (GRU) and fully connected layers with enhanced nonlinear characteristics for pixel-level classification.

Main Results:

LO-MLPRNN achieved high overall accuracies of 99.11% and 99.43% on GF-2 and Sentinel-2 multispectral RS images, respectively.
The algorithm outperformed the Vision Transformer (ViT) by 2.61% and 3.98% on the tested datasets.
Specific classification accuracy for sugarcane reached 99.70% and 99.67%, demonstrating superior performance.

Conclusions:

The LO-MLPRNN algorithm effectively addresses the limitations of traditional deep learning in processing multispectral RS images.
The integration of LSK and ODC modules enables efficient multi-perspective feature fusion and adaptive receptive field optimization.
The proposed method demonstrates superior performance in vegetation cover classification, particularly for specific crop types like sugarcane.