Lie Group spatial attention mechanism model for remote sensing scene classification

ABSTRACT

Utilizing discriminative features to represent data samples is a significant step, and the remote sensing domain is no exception. Most existing convolutional neural network (CNN) models have achieved great results. However, they mainly focus on global high-level features, ignoring local, shallower features and the relationships between features, which are crucial for scene classification. In this study, a novel Lie Group spatial attention mechanism model is introduced. First, it uses Lie Group machine learning and CNN to preserve features at different levels. Then, the Lie Group spatial attention mechanism is used to suppress irrelevant features and enhance local semantic features. Finally, the Lie Fisher classifier is used for prediction. Extensive experiments on two publicly and challenging data sets demonstrate that our model enhances feature characterization capabilities and achieves competitive accuracy with other state-of-the-art methods.

KEYWORDS:

• Attention mechanism
• high-Level features
• scene classification
• shallower features

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was partially supported by the Science and Technology Foundation of Education Department of Jiangxi Province, China, [under Grant No. GJJ203204].