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Updated: Oct 3, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Research on remote sensing image extraction based on deep learning.

Zhao Shun¹, Danyang Li¹, Hongbo Jiang¹

¹Sichuan Agricultural University, College of Information Engineering, Yaan, Sichuan, China.

Peerj. Computer Science

|February 17, 2022

Summary

This summary is machine-generated.

This study enhances remote sensing image segmentation using Landsat 8 data and a novel asymmetric convolution-CBAM module. The deep learning approach significantly improves accuracy for automated land information extraction.

Keywords:

Attention mechanism Automatic extraction Band fusion Semantic segmentation Sliding window prediction

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Area of Science:

Earth Observation
Geospatial Analysis
Artificial Intelligence

Background:

Traditional remote sensing image segmentation struggles with spatial information utilization, leading to high workloads and insufficient accuracy.
Existing methods often fail to fully leverage the rich spatial context present in remote sensing imagery.
Surface resource monitoring relies heavily on remote sensing but faces limitations in current segmentation techniques.

Purpose of the Study:

To improve the accuracy and efficiency of remote sensing image segmentation for land information extraction.
To develop a deep learning model that effectively utilizes spatial information in remote sensing data.
To address the limitations of traditional methods in processing and analyzing satellite imagery.

Main Methods:

Applied data enhancement techniques including atmospheric calibration, band combination, and image fusion to Landsat 8 data.
Developed an asymmetric convolution-CBAM (AC-CBAM) module integrating attention mechanisms and sliding window prediction for deep learning-based segmentation.
Utilized Landsat 8 satellite remote sensing data for experimental validation.

Main Results:

The proposed AC-CBAM module achieved superior segmentation accuracy, with mIoU, mAcc, and aAcc reaching 97.34%, 98.66%, and 98.67%, respectively.
Demonstrated a 1.44% improvement in accuracy compared to the DNLNet model.
The method effectively improved the utilization of spatial information in remote sensing images.

Conclusions:

The AC-CBAM module offers a significant advancement in deep learning for automated remote sensing land information extraction.
The proposed approach provides a robust and accurate solution for enhancing remote sensing image segmentation.
This research offers a valuable reference for developing automated land information extraction systems using deep learning.