Hyperspectral and Lidar Data Classification Based on Linear Self-Attention
Min Feng, Feng Gao, Jian Fang, Junyu Dong
Abstract
In this paper, an efficient linear self-attention fusion model is proposed for the task of hyperspectral image (HSI) and LiDAR data joint classification. The proposed method is comprised of a feature extraction module, an attention module, and a fusion module. The attention module is a plug-and-play linear self-attention module that can be extensively used in any model. The proposed model has achieved the overall accuracy of 95.40% on the Houston dataset. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art models.
Topics & Concepts
Hyperspectral imagingComputer scienceLidarArtificial intelligenceFeature extractionPattern recognition (psychology)Sensor fusionTask (project management)Feature (linguistics)Joint (building)FusionData modelingComputer visionData miningRemote sensingEngineeringGeographySystems engineeringDatabaseArchitectural engineeringLinguisticsPhilosophyRemote-Sensing Image ClassificationAdvanced Image Fusion TechniquesInfrared Target Detection Methodologies