Hyperspectral　image　change　detection　(HSI-CD)　is　a　task　that　utilizes　both　spectral　and　spatial　features　to　more　effectively　detect　changes.　The　features　of　HSI　captured　at　different　times　are　often　influenced　by　external　factors.　The　annoying　variability　is　not　useful　for　CD.　Current　methods　extract　effective　information　through　complex　learning　together　with　it.　They　did　not　focus　on　whether　different　categories　(changed　and　unchanged)　of　sample　pairs　have　the　same　information　composition.　They　lack　a　fundamental　analysis　and　optimization　based　on　the　composition　of　information　pairs　to　address　current　issues　such　as　insufficient　recognition　of　changed　samples　and　poor　feature　fusion.　To　address　these　issues,　we　rethink　the　information　composition　of　bitemporal　sample　pairs　in　different　categories　for　HSI-CD,　and　design　a　frequency-domain　information　exchange　and　generation　network　with　Siamese　U-shaped　structure　(FDIEG-UNet)　for　HSI-CD.　Our　contribution　can　be　summarized　as　follows:　1)　by　designing　the　FDGJLS　module　for　learning　and　separating　global-joint　spectral-spatial　features,　to　provide　better　basic　frequency-domain　information　for　subsequent　feature-domain　optimization;　2)　based　on　the　information　from　FDGJLS,　we　use　the　ULDT　and　SSTDFE　modules　to　remove　the　influence　of　temporal-correlated　invalid　style　information　in　feature　fusion.　Two　modules　work　together　to　improve　the　equality　of　feature　description　between　changed　and　unchanged　samples　in　CD;　and　3)　the　experimental　results　on　three　real　HSI-CD　datasets　demonstrate　the　effectiveness　of　our　proposed　method　in　solving　the　above　problems.　The　code　is　available　at　https://github.com/WUTCM-Lab/FDIEG-UNet.