A Method for Multimodal Remote Sensing Image Classification

In remote sensing, images are widely used in applications, such as land cover classification, urban monitoring, and disaster management, providing rich information about the Earth's surface. However, due to data heterogeneity and scarcity, different modalities of remote-sensing images often face challenges in classification tasks. The proposed deep learning model for remote-sensing image classification addresses these challenges through multimodal fusion. By combining a convolutional neural network, a generative adversarial network, and a graph convolutional network, the model is organized into three main components: data preprocessing and feature extraction, multimodal data generation and enhancement, and multimodal feature fusion and classification. Experimental results on the Hyperspectral-Light Detection and Ranging Houston2013 dataset and the Hyperspectral-Synthetic Aperture Radar Berlin dataset show that the proposed method significantly outperforms traditional methods and other deep learning models in classification performance, with better stability and robustness.