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Related Experiment Video

Updated: Jun 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Multiscale Two-Stream Fusion Network for Benggang Classification in Multi-Source Images.

Xuli Rao^1,2, Chen Feng², Jinshi Lin¹

¹Jinshan Soil and Water Conservation Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This study introduces a novel deep learning method for identifying soil erosion (Benggangs) using drone imagery. The approach accurately maps Benggangs by fusing multiscale features from different data sources, improving land management in South China.

Keywords:

Benggang classification attention mechanism multi-source image fusion multiscale features two-stream fusion network

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Last Updated: Jun 10, 2026

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Geosciences
Remote Sensing
Artificial Intelligence

Background:

Benggangs (soil erosion) are prevalent in South China's hilly regions, complicating land management and ecological efforts.
Accurate identification and mapping of Benggangs are crucial for effective control strategies.
Deep learning offers advanced capabilities for Benggang classification, but challenges remain in feature extraction and fusion from multi-source imagery.

Purpose of the Study:

To develop and evaluate a novel Benggang classification method using multiscale features and a two-stream fusion network (MS-TSFN).
To address the challenge of selecting suitable feature extraction and fusion techniques for multi-source image data in Benggang identification.

Main Methods:

Extracted key geomorphological features (slope, aspect, curvature, hill shade, edge) from drone-acquired Digital Orthophotography Map (DOM) and Digital Surface Model (DSM) data.
Employed a two-stream fusion network with a ResNeSt backbone to extract multiscale features from multi-source images.
Utilized an attention-based feature fusion block for deep information integration and a decision fusion block for final classification.

Main Results:

The proposed MS-TSFN method demonstrated superior performance in extracting spatial features and textures of Benggangs compared to existing approaches.
Optimal results were achieved using a combination of DOM, Canny edge detection, and DSM features.
The model attained high accuracy (92.76%), precision (85.00%), recall (77.27%), and F1-score (0.8059).

Conclusions:

The MS-TSFN method provides an effective solution for Benggang classification, especially in complex terrain.
The fusion of multiscale features from DOM and DSM data significantly enhances identification accuracy.
This approach offers a promising tool for ecological conservation and land management in Benggang-prone areas.