With　the　emergence　of　a　large　number　of　remote　sensing　data　sources,　how　to　effectively　use　the　useful　information　in　multi-source　data　for　better　earth　observation　has　become　an　interesting　but　challenging　problem.　In　this　paper,　the　deep　learning　method　is　used　to　study　the　joint　classification　of　hyperspectral　imagery　(HSI)　and　light　detection　and　ranging　(LiDAR)　data.　The　network　proposed　in　this　paper　is　named　convolutional　neural　network　based　on　multiple　attention　mechanisms　(MatNet).　Specifically,　a　convolutional　neural　network　(CNN)　with　an　attention　mechanism　is　used　to　extract　the　deep　features　of　HSI　and　LiDAR　respectively.　Then　the　obtained　features　are　introduced　into　the　dual-branch　cross-attention　fusion　module　(DCFM)　to　fuse　the　information　in　HSI　and　LiDAR　data　effectively.　Finally,　the　obtained　features　are　introduced　into　the　classification　module　to　obtain　the　final　classification　results.　Experimental　results　show　that　our　proposed　network　can　achieve　better　classification　performance　than　existing　methods.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.