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Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Research on building extraction from remote sensing imagery using efficient lightweight residual network.

Ai Gao¹, Guang Yang¹

¹School of Information Engineering, Institute of Disaster Prevention, Sanhe, China.

Peerj. Computer Science

|June 10, 2024

Summary

This study introduces an efficient lightweight residual network (ELRNet) for accurate automatic building extraction from remote sensing images. ELRNet balances high accuracy with reduced computational demands, making it practical for applications like intelligent city construction.

Keywords:

Building extraction ELRNet Lightweight feature extraction modules Lightweight neural networks Very high-resolution remote sensing images

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Area of Science:

Computer Vision
Remote Sensing
Deep Learning

Background:

Automatic building extraction from high-resolution remote sensing images is crucial for urban planning and emergency response.
Deep learning, particularly Convolutional Neural Networks (CNNs), has advanced building extraction but often requires significant computational resources.
Existing methods face limitations in efficiency and practicality due to high parameter counts and resource demands.

Purpose of the Study:

To propose a novel efficient lightweight residual network (ELRNet) for building extraction that balances accuracy and computational cost.
To design an encoder-decoder architecture incorporating lightweight feature extraction modules (LFEMs) and effective channel attention (ECA).
To demonstrate the practical applicability of ELRNet in real-world remote sensing scenarios.

Main Methods:

Developed ELRNet, an encoder-decoder network featuring downsampling blocks and lightweight feature extraction modules (LFEMs).
Incorporated depthwise-factorised convolution and effective channel attention (ECA) within LFEMs to enhance feature extraction and channel interactions.
Evaluated ELRNet on the WHU Building dataset, comparing its performance against established networks like SegNet and U-Net.

Main Results:

ELRNet achieved an 88.24% Intersection over Union (IoU) on the WHU Building dataset.
The network demonstrated high efficiency with only 2.92 GFLOPs and 0.23 million parameters.
Comparative analysis showed ELRNet outperformed six state-of-the-art baseline networks in accuracy-efficiency tradeoff.

Conclusions:

ELRNet provides a superior balance between accuracy and computational efficiency for automatic building extraction from very high-resolution remote sensing images.
The proposed network design is suitable for practical applications where computational resources are a constraint.
The study offers a valuable contribution to the field of remote sensing image analysis and intelligent urban development.