Remote-sensing　(RS)　scene　classification　is　a　fundamental　and　significant　task　in　RS　image　interpretation,　involving　the　annotation　of　semantic　content.　RS　scene　images　are　characterized　by　complex　backgrounds,　rich　content,　and　multiscale　targets,　exhibiting　both　intraclass　separation　and　interclass　convergence.　Therefore,　extracting　features　that　effectively　express　the　intrinsic　attributes　of　images　and　possess　high　discriminative　is　crucial　for　RS　scene　classification.　Existing　global-based　methods　often　lack　the　ability　to　capture　significant　detailed　information　in　similar　scenes.　Conversely,　methods　based　on　local　discriminative　features　tend　to　overlook　the　interrelationships　of　objects　within　the　same　scene.　To　address　these　issues,　this　article　proposes　a　unified　framework　named　MBFNet　to　align　and　fuse　features　of　different　scales　and　levels　for　accurate　RS　scene　classification.　We　utilize　a　multibranch　feature-extracting　network　structure　with　parallel　convolution　and　Transformer　modules.　Simultaneously,　a　kernel-selected　multiscale　aggregation　(KSMSA)　module　is　designed　to　efficiently　process　the　diverse　scale　features　emanating　from　these　parallel　branches.　By　selecting　different　convolution　kernels,　a　dynamic　receptive　field　is　established　to　adaptively　process　features　of　different　scales,　reducing　semantic　differences　to　achieve　effective　aggregation　of　multiscale　features.　Moreover,　a　learnable　multilevel　aggregation　(LMLA)　module　is　designed　to　integrate　shallow　features,　such　as　shape　information,　into　deep　features　for　more　comprehensive　feature　fusion.　Benefiting　from　KSMSA　and　LMLA,　the　proposed　MBFNet　improves　the　discriminability　of　features,　thereby　enhancing　classification　performance.　Comprehensive　experiments　on　three　benchmark　datasets　demonstrate　that　the　proposed　method　outperforms　state-of-the-art　RS　scene　classification　methods　in　terms　of　performance.