To　mitigate　the　impact　of　noisy　labels,　many　methods　prioritize　simple　samples　with　reliable　labels,　often　overlooking　the　valuable　information　in　more　challenging　samples.　This　study　introduces　SRODET,　a　novel　semi-supervised　remote　sensing　object　detection　model　that　leverages　sample　complexity　to　extract　accurate　pseudo-labeled　knowledge.　We　employ　a　dual-branch　structure　(DBS)　to　generate　reliable　pseudo　labels　for　auxiliary　supervision,　enhancing　joint　supervision　to　derive　high-quality　pseudo　labels　from　low-confidence　predictions.　This　approach　reduces　the　risk　of　losing　object　instances　due　to　low-confidence　scores,　particularly　for　extreme　scales.　Additionally,　we　introduce　a　pseudo-label　training　strategy　based　on　sample　difficulty,　evaluating　complexity　through　object　uncertainty　and　angular　information　from　remote　sensing　images.　Our　experimental　results　show　that　SRODET　achieves　state-of-the-art　performance　in　semi-supervised　remote　sensing　object　detection　across　various　settings　in　the　DOTA-v1.5　and　HRSC2016　benchmarks.