Faced　with　the　wide-scale　characteristics　of　objects　in　optical　remote　sensing　images,　the　current　object　detection　models　are　always　unable　to　provide　satisfactory　detection　capabilities　for　remote　sensing　tasks.　To　achieve　better　wide-scale　coverage　for　various　remote　sensing　regions　of　interest,　this　article　introduces　a　multiprediction　mechanism　to　build　a　novel　region　generation　model,　namely,　a　multiple　region　proposal　experts　network　(MRPENet).　Meanwhile,　to　achieve　both　region　proposal　coverage　and　receptive　field　coverage　of　wide-scale　objects,　we　constructed　a　prior　design　of　an　anchor　(PDA)　module　and　an　adaptive　features　compensation　(AFC)　module　to　achieve　the　coverage　of　wide-scale　remote　sensing　objects.　To　better　utilize　the　multiexpert　characteristics　of　our　model,　we　customized　a　new　training　sample　allocation　strategy,　dynamic　scale-assigned　expert　learning　(DSAEL),　to　cultivate　the　ability　of　experts　to　deal　with　objects　at　various　scales.　To　the　best　of　our　knowledge,　this　is　the　first　time　that　a　multiple　region　proposal　network　(RPN)　mechanism　has　been　used　in　the　object　detection　of　optical　remote　sensing　images.　Extensive　experiments　have　shown　the　generality　and　effectiveness　of　our　MRPENet.　Without　bells　and　whistles,　MRPENet　achieves　a　new　state-of-the-art　(SOTA)　on　standard　benchmarks,　i.e.,　DOTA-v1.0　[82.02%　mean　average　precision　(mAP)],　HRSC2016　(98.16%　mAP),　and　FAIR1M-v1.0　(48.80%　mAP).