Download PDFOpen PDF in browserMultimodal Remote Sensing Classification with Cascaded Attention Convolution Neural NetworkEasyChair Preprint 81536 pages•Date: May 31, 2022AbstractMultimodal remote sensing classification tasks always encounter the data problem of unbalanced feature distributions from various information sources. In this paper, we adopt the attention mechanism with a cascaded multi-scale training strategy to enhance the performance feature extraction of one data source. We have utilized the hyperspectral and LiDAR data to provide the proposed algorithm's efficiency with multimodal Trento dataset. Finally, we have achieved better classification performance on the ground object categories with close similarity on height features owing to strengthening the feature extraction by our methodology. Keyphrases: Cascaded Convolution Neural Network, Multimodal Remote Sensing Classification, attention module
|