For　cross-modal　remote　sensing　image-audio　(RSIA)　retrieval　task,　hashing　technology　has　attracted　much　attention　in　recent　works.　Most　of　them　focus　on　mapping　RS　images　and　audios　into　a　Hamming　space,　whilst　neglecting　discriminative　information　of　RS　images　and　fine　alignment　for　RS　images　and　audios.　In　this　article,　we　tackle　these　dilemmas　with　a　novel　fine　aligned　discriminative　hashing　(FADH)　approach,　which　can　learn　hash　codes　to　capture　discriminative　information　of　RS　images　and　learn　the　corresponding　detailed　information　between　RS　images　and　audios　simultaneously.　We　first　develop　a　new　discriminative　information　learning　module　to　learn　discriminative　information　about　RS　images.　Meanwhile,　a　fine　alignment　module　is　proposed　to　unearth　the　fine　correspondence　for　RS　image　regions　and　audios,　which　can　effectively　improve　the　retrieval　performance.　On　top　of　the　two　paths,　we　design　a　new　objective　function,　which　can　maintain　the　similarity　of　hash　codes,　preserve　the　semantic　information　of　RS　image　features　and　audio　features　and　eliminate　cross-modal　differences.　The　reliability　and　significance　of　the　designed　framework　are　effectively　demonstrated　by　diverse　experiments　on　three　RSIA　datasets.