Remote　sensing　of　rotated　objects　often　encounters　numerous　small　and　dense　objects.　To　tackle　small-object　neglect　and　inaccurate　angle　predictions　in　elongated　objects,　we　propose　SA3Det,　a　novel　method　employing　Pixel-Level　Attention　and　Adaptive　Labels　Assignment.　First,　we　introduce　a　self-attention　module　that　learns　dense　pixel-level　relations　between　features　extracted　by　the　backbone　and　neck,　effectively　preserving　and　exploring　the　spatial　relationships　of　potential　small　objects.　We　then　introduce　an　adaptive　label　assignment　strategy　that　refines　proposals　by　assigning　labels　based　on　loss,　enhancing　sample　selection　during　training.　Additionally,　we　designed　an　angle-sensitive　module　that　enhances　angle　prediction　by　learning　rotational　feature　maps　and　incorporating　multi-angle　features.　These　modules　significantly　enhance　detection　accuracy　and　yield　high-quality　region　proposals.　Our　approach　was　validated　by　experiments　on　the　DOTA　and　HRSC2016　datasets,　demonstrating　that　SA3Det　achieves　mAPs　of　76.31%　and　89.4%,　respectively.