Establishing　reliable　correspondences　is　a　fundamental　task　in　computer　vision,　and　it　requires　rich　contextual　information.　In　this　paper,　we　propose　a　Channel-Spatial　Difference　Augment　Network　(CSDA-Net),　by　selectively　aggregating　information　from　spatial　and　channel　aspects,　to　seek　reliable　correspondences　for　feature　matching.　Specifically,　we　firstly　introduce　the　spatial　and　channel　attention　mechanism　to　construct　a　simple　yet　effective　block　for　discriminately　extracting　the　global　context.　After　that,　we　design　a　Overlay　Attention　block　by　further　exploiting　the　spatial　and　channel　attention　mechanism　with　different　squeeze　operations,　to　gather　more　comprehensive　contextual　information.　Finally,　the　proposed　CSDA-Net　is　able　to　achieve　feature　maps　with　a　strong　representative　ability　for　feature　matching　due　to　the　integration　of　the　two　novel　blocks.　Extensive　experiments　on　outlier　rejection　and　relative　pose　estimation　have　shown　better　performance　improvements　of　our　CSDA-Net　over　current　state-of-the-art　methods　on　both　outdoor　and　indoor　datasets.　©　2022　Elsevier　Ltd