Deep　convolutional　neural　networks　have　been　widely　used　in　scene　classification　of　remotely　sensed　images.　In　this　work,　we　propose　a　robust　learning　method　for　the　task　that　is　secure　against　partially　incorrect　categorization　of　images.　Specifically,　we　remove　and　correct　errors　in　the　labels　progressively　by　iterative　multi-view　voting　and　entropy　ranking.　At　each　time　step,　we　first　divide　the　training　data　into　disjoint　parts　for　separate　training　and　voting.　The　unanimity　in　the　voting　reveals　the　correctness　of　the　labels,　so　that　we　can　train　a　strong　model　with　only　the　images　with　unanimous　votes.　In　addition,　we　adopt　entropy　as　an　effective　measure　for　prediction　uncertainty,　in　order　to　partially　recover　labeling　errors　by　ranking　and　selection.　We　empirically　demonstrate　the　superiority　of　the　proposed　method　on　the　WHU-RS19　dataset　and　the　AID　dataset.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.