We　introduce　LCMatch,　a　novel　semi-supervised　scene　classification　framework　designed　to　enhance　the　performance　of　remote　sensing　image　classification.　Our　method　improves　upon　the　existing　FixMatch　framework　by　incorporating　a　hierarchical　structure　for　pseudo-label　generation.　The　framework　consists　of　three　key　modules:　hierarchical　cross-random　combination　(HCRC),　adaptive　weighting　mechanism,　and　label　alignment.　These　modules　work　synergistically　to　generate　high-quality　pseudo-labels,　refining　model　predictions,　and　adaptively　balancing　the　contributions　of　labeled　and　unlabeled　data　during　training.　In　addition,　we　conduct　extensive　experiments　on　three　widely　used　remote　sensing　datasets,　including　AID,　UCMerced,　and　NWPU-RESISC45.　Results　demonstrate　that　LCMatch　outperforms　state-of-the-art　semi-supervised　learning　(SSL)　methods　in　terms　of　classification　accuracy.　Specifically,　LCMatch　exhibits　robust　performance　even　with　a　very　limited　number　of　labeled　samples,　also　effectively　handling　class　imbalance　and　distinguishing　challenging　categories.