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Updated: Sep 16, 2025

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Turbulence-Resilient Object Classification in Remote Sensing Using a Single-Pixel Image-Free Approach.

Yin Cheng^1,2,3, Yusen Liao^1,2,3, Jun Ke^1,2,3

¹School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|July 12, 2025

Summary

This summary is machine-generated.

This study introduces an image-free classification method using single-pixel imaging (SPI) for remote sensing. The novel framework achieves robust object classification even with atmospheric turbulence and low signal conditions.

Keywords:

atmospheric turbulence image processing object classification single pixel imaging

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Area of Science:

Optics and Photonics
Computer Vision
Remote Sensing

Background:

Remote sensing object classification is challenged by atmospheric turbulence and low signals.
Traditional image reconstruction methods are computationally intensive and unreliable in degraded conditions.

Purpose of the Study:

To develop a novel image-free classification framework for robust object recognition in challenging remote sensing environments.
To bypass computationally expensive image reconstruction for direct classification from 1D measurements.

Main Methods:

A single-pixel imaging (SPI) framework utilizing a learnable sampling matrix for structured light modulation.
A hybrid Convolutional Neural Network (CNN)-Transformer network (Hybrid-CTNet) for robust feature extraction.
A (N+1)×L hybrid strategy integrating convolutional and Transformer blocks for enhanced resilience.

Main Results:

The proposed method demonstrates superior classification accuracy and computational efficiency compared to existing image-based and image-free approaches.
Effectiveness validated through extensive simulations and optical experiments under varying turbulence intensities.
Successful classification achieved at sampling rates as low as 1%.

Conclusions:

The novel image-free SPI framework offers a robust and efficient solution for object classification in degraded remote sensing conditions.
The Hybrid-CTNet architecture enhances resilience against atmospheric turbulence.
Potential for low-resource, real-time remote sensing applications is highlighted.