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Remote Sensing Image Recognition Based on LOG-T-SSA-LSSVM and AE-ELM Network.

Chang-Jian Sun¹, Fang Gao^2,3

¹College of Electronic Science and Engineering, Jilin University, Changchun 130012, China.

Computational Intelligence and Neuroscience

|February 7, 2022

Summary

This summary is machine-generated.

This study introduces a novel LOG-T-SSA-LSSVM classification network and an autoencoder-extreme learning machine (AE-ELM) for remote sensing image recognition. The combined approach significantly enhances recognition accuracy under varying conditions compared to traditional methods.

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Area of Science:

Computer Science
Remote Sensing
Machine Learning

Background:

Remote sensing image recognition accuracy is affected by diverse working conditions.
Existing methods often struggle with complex environmental variations.
Hierarchical strategies offer a potential solution for improved classification.

Purpose of the Study:

To develop and evaluate a novel hierarchical network for enhanced remote sensing image recognition.
To improve classification accuracy and recognition performance under different working conditions.
To compare the proposed method against traditional and existing advanced techniques.

Main Methods:

A Logistic-T-distribution-Sparrow Search Algorithm-Least Squares Support Vector Machines (LOG-T-SSA-LSSVM) network was proposed for sample classification and parameter optimization.
An autoencoder-extreme learning machine (AE-ELM) was integrated for data compression and efficient supervised recognition.
The AE-ELM network was optimized using the sigmoid activation function and 2000 hidden layer neurons.

Main Results:

The LOG-T-SSA-LSSVM network demonstrated significantly improved classification accuracy on the UCI dataset compared to contrast networks.
The AE-ELM network showed good recognition performance, particularly with specific configurations.
The combined AE-ELM based on LOG-T-SSA-LSSVM classification achieved substantial improvements in recognition accuracy over traditional ELM and PSO-ELM networks.

Conclusions:

The proposed LOG-T-SSA-LSSVM and AE-ELM integration offers a robust solution for remote sensing image recognition.
This hierarchical approach effectively addresses challenges posed by varying working conditions.
The findings indicate a significant advancement in recognition accuracy for remote sensing applications.