Feature-based　change　detection　technologies　using　multitemporal　remote　sensing　images　are　widely　applied　to　find　newly　increased　built-up　areas　(NIBUA)　during　the　period　of　observation.　This　paper　proposes　an　automatic　object-based　NIBUA　extraction　method　using　high-resolution　remote　sensing　images,　which　is　based　on　the　integration　of　spectrum　feature,　edge-derived　line-density-based　visual　saliency　(LDVS)　feature,　and　texture-derived　built-up　presence　index　(PanTex)　feature.　In　the　proposed　method,　image　segmentation　is　first　employed　to　obtain　objects　as　basic　units　of　detection.　Next,　due　to　the　complexity　of　built-up　areas　in　high-resolution　images,　LDVS　images　and　PanTex　images　are　produced　for　each　temporal　image,　respectively.　Then,　to　highlight　built-up　areas　in　complex　scenes,　a　comprehensive　measure　for　each　object　is　calculated　by　integrating　the　newly　increased　measures　from　spectrum,　LDVS,　and　PanTex　features　via　a　manner　of　Dempster-Shafer　evidence　fusion.　Finally,　the　object-based　NIBUA　can　be　extracted　by　conducting　binarization　on　the　newly　increased　fused　measure　image.　Comparison　studies　and　experimental　results　demonstrate　that　our　method　can　achieve　a　robust　extraction　of　NIBUA　from　high-resolution　remote　sensing　images　with　a　higher　detection　accuracy.　We　conclude　that　this　automatic　way　can　play　a　positive　role　in　reducing　the　artificial　workload　of　the　interpreters　and　the　cost　of　monitoring　a　large-region　area.　It　is　encouraged　to　employ　this　method　in　a　variety　of　applications,　such　as　illegal　construction　land　monitoring,　land　use/cover　map　update,　and　city　planning.　(C)　2018　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE).