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Looking Closer at the Scene: Multiscale Representation Learning for Remote Sensing Image Scene Classification.

Qi Wang, Wei Huang, Zhitong Xiong

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This study introduces a novel two-stream architecture for remote sensing image scene classification. The method effectively handles large-scale variations by integrating global and local features, achieving state-of-the-art results.

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Area of Science:

Computer Vision
Geospatial Analysis
Machine Learning

Background:

Remote sensing image scene classification is crucial for various applications.
Convolutional Neural Network (CNN) methods excel but struggle with scale variations in remote sensing data.
Existing approaches face limitations due to the diverse scales of features and objects.

Purpose of the Study:

To improve remote sensing image scene classification performance.
To address the challenge of large-scale feature and object variations.
To develop a multiscale representation approach for enhanced classification.

Main Methods:

A global-local two-stream architecture is proposed.
The architecture extracts global features from the whole image and local features from key areas.
A weakly supervised strategy, structured key area localization (SKAL), is introduced for key area detection using image-level labels.

Main Results:

The SKAL-based two-stream architecture achieved state-of-the-art results on four public datasets.
Comparative experiments were conducted using AlexNet, GoogleNet, and ResNet18.
The method demonstrated superior performance in classifying remote sensing image scenes.

Conclusions:

The proposed SKAL-based two-stream architecture effectively enhances remote sensing image scene classification.
Multiscale representation is key to overcoming limitations posed by scale variations.
The approach offers a significant advancement in the field, validated by state-of-the-art performance.