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Updated: Jan 18, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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A Hierarchical Multi-Feature Point Cloud Lithology Identification Method Based on Feature-Preserved Compressive

Xiaolei Duan¹, Ran Jing¹, Yanlin Shao¹

¹School of Geosciences, Yangtze University, Wuhan 430100, China.

Sensors (Basel, Switzerland)

|September 13, 2025

Summary

This summary is machine-generated.

This study introduces a novel algorithm for accurate lithology identification using 3D point cloud data. The Feature-Preserved Compressive Sampling (FPCS) method significantly enhances accuracy in complex geological areas.

Keywords:

laser point cloud lithology identification machine learning random forest

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Area of Science:

Geoscience
Computer Science
Remote Sensing

Background:

Traditional lithology identification methods struggle with complex geological terrains, leading to low accuracy.
Challenges include weathering, vegetation cover, and spectral overlap in sedimentary rock regions.

Purpose of the Study:

To develop a high-precision lithology identification model using 3D laser point cloud data.
To overcome limitations of existing methods in feature representation and classification accuracy.

Main Methods:

A hierarchical multi-feature random forest algorithm based on Feature-Preserved Compressive Sampling (FPCS) was proposed.
Integrated graph signal processing and multi-scale feature fusion using a geologically adaptive graph model.
Employed a three-tier graph filter bank and dynamic gated fusion for feature extraction and optimization.

Main Results:

Achieved an overall accuracy (OA) of 95.6% and mean accuracy (mAcc) of 94.3% on five million test samples.
Demonstrated significant improvements of 36.1% (OA) and 20.5% (mAcc) compared to the PointNet model.
Successfully improved identification accuracy in challenging lithological transition zones.

Conclusions:

The FPCS-based hierarchical multi-feature approach offers robust engineering applicability for point cloud lithology identification.
This method effectively addresses challenges in complex sedimentary rock regions.
Confirms the potential for enhanced geological resource exploration and engineering safety assessment.