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Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Updated: Aug 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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Published on: December 15, 2023

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Semisupervised Change Detection Based on Bihierarchical Feature Aggregation and Extraction Network.

Mingyang Zhang, Tianqi Gao, Maoguo Gong

    IEEE Transactions on Neural Networks and Learning Systems
    |April 6, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new semisupervised change detection framework for high-resolution remote sensing images. It effectively combines pixel and object features using a novel network, improving accuracy with limited labeled data.

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    Area of Science:

    • Geosciences
    • Computer Science
    • Artificial Intelligence

    Background:

    • High-resolution remote sensing (RS) image change detection (CD) is crucial for various applications.
    • Pixel-based CD methods are prone to noise, while object-based methods may miss details.
    • Combining these approaches and addressing the scarcity of labeled data are key challenges.

    Purpose of the Study:

    • To propose a novel semisupervised change detection framework for high-resolution RS images.
    • To integrate pixel-level and object-level features for comprehensive analysis.
    • To overcome the limitations of insufficient labeled data in supervised CD.

    Main Methods:

    • A bi-hierarchical feature aggregation and extraction network (BFAEN) is designed for pixelwise and objectwise feature concatenation.
    • A confident learning algorithm is employed to handle noisy labels.
    • A novel loss function facilitates training with both true and pseudo-labels in a semisupervised manner.

    Main Results:

    • The proposed semisupervised framework demonstrates effectiveness in high-resolution RS image change detection.
    • The BFAEN successfully integrates multi-level features for improved detection.
    • Experimental results confirm the method's superiority over existing approaches.

    Conclusions:

    • The developed semisupervised CD framework offers a robust solution for high-resolution imagery.
    • The integration of pixel and object features enhances change detection capabilities.
    • This approach effectively utilizes limited labeled data for accurate results.