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Scene Recognition Using Deep Softpool Capsule Network Based on Residual Diverse Branch Block.

Chunyuan Wang¹, Yang Wu², Yihan Wang¹

¹School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

Sensors (Basel, Switzerland)

|August 28, 2021

Summary

This study introduces DS-CapsNet, a novel capsule network for remote sensing (RS) image scene recognition. It enhances feature representation and reduces overfitting, achieving superior performance on complex datasets.

Keywords:

capsule network diverse branch block residual convolution scene recognition squeeze and excitation

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Area of Science:

Computer Vision
Remote Sensing Image Analysis
Machine Learning

Background:

Remote sensing (RS) image scene recognition is crucial but challenging due to complex backgrounds and bird's eye view acquisition.
Existing capsule networks struggle with complex backgrounds, limiting their effectiveness in RS image recognition.

Purpose of the Study:

To propose a novel end-to-end capsule network, DS-CapsNet, for improved remote sensing scene recognition.
To enhance the discriminative representation of diverse and complex RS scenes.

Main Methods:

Developed DS-CapsNet integrating a multi-scale feature enhancement module and Caps-SoftPool.
Employed residual convolution architecture, Diverse Branch Block (DBB), and Squeeze and Excitation (SE) block for feature extraction and fusion.
Introduced Caps-SoftPool to reduce parameters and prevent overfitting.

Main Results:

DS-CapsNet achieved competitive and promising performance in RS image recognition.
The model demonstrated robustness and superiority on the AID and NWPU-RESISC45 datasets.
High-quality and robust capsule representation contributed to improved recognition accuracy.

Conclusions:

The proposed DS-CapsNet effectively addresses challenges in remote sensing scene recognition.
The novel architecture enhances feature representation and parameter efficiency.
DS-CapsNet offers a robust solution for high-quality remote sensing image analysis.