Skip-Connected Covariance Network for Remote Sensing Scene Classification.

Area of Science:

• Computer Science
• Remote Sensing
• Artificial Intelligence

Background:

• Remote Sensing Scene Classification (RSSC) is crucial for analyzing satellite imagery.
• Traditional Convolutional Neural Networks (CNNs) face challenges with large-scale variance in RSSC datasets.
• Existing methods often require a large number of parameters, limiting efficiency.

Purpose of the Study:

• To propose a novel end-to-end learning model, the skip-connected covariance (SCCov) network, for enhanced RSSC.
• To improve feature learning by integrating skip connections and covariance pooling into CNNs.
• To achieve competitive or superior classification performance with reduced model complexity.

Main Methods:

• Developed the SCCov network by embedding skip connections and covariance pooling modules into a CNN architecture.
• Utilized skip connections to merge multi-resolution feature maps, addressing scale variance.
• Employed covariance pooling to extract second-order statistical information from feature maps.

Main Results:

• The SCCov network demonstrated highly competitive or superior classification performance on three large-scale benchmark datasets.
• The proposed model achieved state-of-the-art results in RSSC.
• SCCov required only 10% of the parameters compared to existing methods, indicating significant efficiency gains.

Conclusions:

• The SCCov network offers an effective and efficient solution for remote sensing scene classification.
• The integration of skip connections and covariance pooling enhances feature representation and classification accuracy.
• The model's reduced parameter count makes it a practical advancement for RSSC applications.