With　the　joint　advancement　of　numerous　related　fields　of　remote　sensing,　the　amount　of　remote　sensing　data　is　growing　exponentially.　As　an　essential　remote　sensing　Big　Data　management　technique,　content-based　remote　sensing　image　retrieval　has　attracted　more　and　more　attention.　A　novel　deep　attention　hashing　with　distance-adaptive　ranking　(DAH)　is　proposed　for　remote　sensing　image　retrieval　in　this　article.　First,　a　channel-spatial　joint　attention　mechanism　is　employed　for　feature　extraction　of　remote　sensing　images　to　make　the　proposed　DAH　method　focus　more　on　the　critical　details　of　the　remote　sensing　images　and　suppress　irrelevant　regional　responses.　Second,　a　novel　balanced　pairwise　weighted　loss　function　is　proposed　to　enable　discrete　hash　codes　to　participate　in　neural　network　training,　which　contains　pairwise　weighted　similarity　loss,　classification　loss,　and　quantization　loss.　The　pairwise　weighted　similarity　loss　is　designed　to　decrease　the　impact　of　the　imbalance　of　positive　and　negative　sample　pairs.　The　classification　loss　and　quantization　loss　are　added　to　the　loss　function　to　decrease　background　interference　and　information　loss　during　the　quantization　phase,　respectively.　Finally,　a　distance-adaptive　ranking　strategy　with　category-weighted　Hamming　distance　is　presented　in　the　retrieval　phase　to　utilize　the　category　probability　information　fully.　Experiments　on　benchmark　datasets　compared　with　state-of-the-art　methods　demonstrate　the　effectiveness　of　the　proposed　DAH　method.