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Cloud Detection in Remote Sensing Images Based on a Novel Adaptive Feature Aggregation Method.

Wanting Zhou¹, Yan Mo^1,2, Qiaofeng Ou¹

¹School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This summary is machine-generated.

A new network model, NFCNet, improves cloud detection in remote sensing. It accurately identifies cloud boundaries and thin clouds, even in complex conditions, outperforming existing methods.

Keywords:

adaptive feature aggregation cloud detection feature fusion multi-scale

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Area of Science:

Remote Sensing
Computer Vision
Artificial Intelligence

Background:

Cloud detection is crucial for remote sensing data preprocessing.
Accurate identification of cloud boundaries and thin clouds remains challenging, especially in complex scenarios.

Purpose of the Study:

To design and evaluate NFCNet, a novel network model for enhanced cloud detection.
To improve the accuracy of cloud boundary segmentation and thin cloud localization.

Main Methods:

NFCNet incorporates three key submodules: Hybrid Convolutional Attention Module (HCAM), Spatial Pyramid Fusion Attention (SPFA), and Dual-Stream Convolutional Aggregation (DCA).
HCAM extracts multi-scale features and prioritizes critical information.
SPFA adaptively fuses features to recover lost details and reinforce important information during upsampling.
DCA integrates high-level and low-level features to maintain sensitivity to fine details.

Main Results:

NFCNet demonstrated superior performance on the HRC_WHU, CHLandsat8, and 95-Cloud datasets.
The proposed algorithm achieved finer segmentation of cloud boundaries compared to existing optimal methods.
NFCNet provided more precise localization of subtle thin clouds.

Conclusions:

NFCNet effectively addresses the challenges in cloud boundary detection and thin cloud identification.
The network's architecture enables more accurate and detailed cloud segmentation in remote sensing imagery.