This　letter　addresses　the　issue　of　accurate　object　detection　in　large-area　remote　sensing　images.　Although　many　convolutional　neural　network　(CNN)-based　object　detection　models　can　achieve　high　accuracy　in　small　image　patches,　the　models　perform　poorly　in　large-area　images　due　to　the　large　quantity　of　false　and　missing　detections　that　arise　from　complex　backgrounds　and　diverse　groundcover　types.　To　address　this　challenge,　this　letter　proposes　a　sample　update-based　CNN　(SUCNN)　framework　for　object　detection　in　large-area　remote　sensing　images.　The　proposed　framework　contains　two　stages.　In　the　first　stage,　a　base　model-single-shot　multibox　detector-is　trained　with　the　training　data　set.　In　the　second　stage,　artificial　composite　samples　are　generated　to　update　the　training　set.　The　parameters　of　the　first-stage　model　are　fine-tuned　with　the　updated　data　set　to　obtain　the　second-stage　model.　The　first-　and　second-stage　models　are　evaluated　using　the　large-area　remote　sensing　image　test　set.　Comparison　experiments　show　the　effectiveness　and　superiority　of　the　proposed　SUCNN　framework　for　object　detection　in　large-area　remote　sensing　images.