Multimodal data combination can effectively improve the accuracy of ground object classification.In view of the limitations of traditional multimodal collaborative classification algorithms in heterogeneous feature representation,this paper proposes a classification algorithm based on twin collaborative attention adversarial network(MCA2 Net),using for the fusion classification of high-resolution remote sensing images and LiDAR data.MCA2 Net first designed multi-scale feature extraction and multi-attention mechanism modules,combined with the adversarial game process to effectively extract high-order semantic features and differentiated complementary information,to achieve discriminative retention of detailed features of multi-modal data;at the same time,it integrated adversarial and classification tasks to build a composite loss functions are used for collaborative training to balance unsupervised image fusion tasks and supervised classification tasks to improve model stability.Experimental results based on the Houston and Yunnan-mountain data sets show that the MCA2N et al gorithm can effectively improve the collaborative classification performance of multi-modal data.The research results can be applied to urban planning,environmental monitoring,land use classification and other fields.
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