Object　detection　in　remote　sensing　images　(RSIs),　including　optical　and　SAR　images,　has　emerged　as　a　rapidly　advancing　field.　However,　the　abundance　of　small　objects　in　RSIs　poses　a　significant　challenge　in　designing　a　network　structure　with　effective　receptive　fields　to　support　accurate　localization　and　classification.　In　this　paper,　we　propose　a　position　guided　dynamic　receptive　field　network　(PG-DRFNet)　for　small　object　detection　friendly　to　optical　and　SAR　images.　Specifically,　PG-DRFNet　overcomes　the　problem　of　small　objects　vanishing　or　being　submerged　in　features　by　establishing　a　positional　guidance　relationship　of　small　objects　between　different　feature　layers.　Then,　we　design　a　combination　head　structure　that　utilizes　additional　supervised　information　extracted　from　small　objects　to　make　the　model　more　effective　and　flexible.　Moreover,　a　dynamic　perception　algorithm　based　on　feature　construction　are　developed　to　dynamically　optimize　the　perception　regions　and　feature　hierarchies　of　the　model,　while　seeking　the　optimal　tradeoff　between　model　accuracy　and　inference　speed.　Without　bells　and　whistles,　our　model　is　robust　to　two　modalities　of　remote　sensing　data,　and　our　experiments　are　conducted　on　four　benchmark　RSI　datasets,　including　DOTA-v2.0,　VEDAI,　SSDD,　and　HRSID.　The　experimental　results　achieve　competitive　performance　with　59.01%,　84.06%,　90.06%,　and　80.59%　mAP,　respectively.　Code　and　models　are　released　at　https://github.com/BJUT-AIVBD/PG-DRFNet.　©　2025　IEEE.