Due　to　the　complementary　properties　of　different　types　of　sensors,　change　detection　between　heterogeneous　images　receives　increasing　attention　from　researchers.　However,　change　detection　cannot　be　handled　by　directly　comparing　two　heterogeneous　images　since　they　demonstrate　different　image　appearances　and　statistics.　In　this　article,　we　propose　a　deep　pyramid　feature　learning　network　(DPFL-Net)　for　change　detection,　especially　between　heterogeneous　images.　DPFL-Net　can　learn　a　series　of　hierarchical　features　in　an　unsupervised　fashion,　containing　both　spatial　details　and　multiscale　contextual　information.　The　learned　pyramid　features　from　two　input　images　make　unchanged　pixels　matched　exactly　and　changed　ones　dissimilar　and　after　transformed　into　the　same　space　for　each　scale　successively.　We　further　propose　fusion　blocks　to　aggregate　multiscale　difference　images　(DIs),　generating　an　enhanced　DI　with　strong　separability.　Based　on　the　enhanced　DI,　unchanged　areas　are　predicted　and　used　to　train　DPFL-Net　in　the　next　iteration.　In　this　article,　pyramid　features　and　unchanged　areas　are　updated　alternately,　leading　to　an　unsupervised　change　detection　method.　In　the　feature　transformation　process,　local　consistency　is　introduced　to　constrain　the　learned　pyramid　features,　modeling　the　correlations　between　the　neighboring　pixels　and　reducing　the　false　alarms.　Experimental　results　demonstrate　that　the　proposed　approach　achieves　superior　or　at　least　comparable　results　to　the　existing　state-of-the-art　change　detection　methods　in　both　homogeneous　and　heterogeneous　cases.