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Related Experiment Video

Updated: Dec 26, 2025

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

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

Published on: December 15, 2023

Three-Dimensional ResNeXt Network Using Feature Fusion and Label Smoothing for Hyperspectral Image Classification.

Peida Wu¹, Ziguan Cui¹, Zongliang Gan¹

¹College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Sensors (Basel, Switzerland)

|March 20, 2020

Summary

This study introduces a 3D-ResNeXt network for hyperspectral image (HSI) classification, improving accuracy and reducing parameters. The new method enhances classification for small sample categories using spectral-spatial feature learning and label smoothing.

Keywords:

deep learning group convolution hyperspectral image classification spectral-spatial features

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Area of Science:

Remote Sensing
Computer Vision
Artificial Intelligence

Background:

Deep learning, particularly 3D convolution methods, shows promise for hyperspectral image (HSI) classification.
Existing 3D convolution approaches face challenges with increased parameters and underutilization of spectral information, especially for small sample classes.

Purpose of the Study:

To develop an efficient deep learning model for HSI classification that addresses parameter explosion and improves accuracy for imbalanced datasets.
To enhance the extraction of discriminative spectral-spatial features from HSIs.

Main Methods:

Designed an end-to-end 3D-ResNeXt network incorporating residual connections and a split-transform-merge strategy.
Implemented feature fusion and an additional spectral feature learning module to enrich input data.
Utilized a modified loss function with a label smoothing strategy to handle class imbalance.

Main Results:

The proposed 3D-ResNeXt network effectively reduces the number of parameters compared to traditional 3D CNNs.
Achieved superior classification accuracies on three HSI datasets, notably improving performance for classes with limited training samples.
Demonstrated the effectiveness of adjusting cardinality over network depth for feature extraction.

Conclusions:

The novel 3D-ResNeXt network offers a significant advancement in HSI classification by balancing efficiency and accuracy.
The integrated feature fusion, spectral learning, and label smoothing strategies successfully mitigate challenges associated with small sample sizes and class imbalance.