The　popular　Siamese　convolutional　neural　networks　(CNNs)　for　remote　sensing　(RS)　image　change　detection　(CD)　often　suffer　from　two　problems.　First,　they　either　ignore　the　original　information　of　bitemporal　images　or　insufficiently　utilize　the　difference　information　between　bitemporal　images,　which　leads　to　the　low　tightness　of　the　changed　objects.　Second,　Siamese　CNNs　always　employ　dual-branch　encoders　for　CD,　which　increases　computational　cost.　To　address　the　above　issues,　this　article　proposes　a　network　based　on　difference　enhancement　and　spatial　&　x2013;spectral　nonlocal　(DESSN)　for　CD　in　very-high-resolution　(VHR)　images.　This　article　makes　threefold　contributions.　First,　we　design　a　difference　enhancement　(DE)　module　that　can　effectively　learn　the　difference　representation　between　foreground　and　background　to　reduce　the　impact　of　irrelevant　changes　on　the　detection　results.　Second,　we　present　a　spatial　&　x2013;spectral　nonlocal　(SSN)　module　that　is　different　from　vanilla　nonlocal　because　multiscale　spatial　global　features　are　incorporated　to　model　the　large-scale　variation　of　objects　during　CD.　The　module　can　be　used　to　strengthen　the　edge　integrity　and　internal　tightness　of　changed　objects.　Third,　the　asymmetric　double　convolution　with　Ghost　(ADCG)　module　is　exploited　instead　of　standard　convolution.　The　ADCG　can　not　only　refine　the　edge　information　of　the　changed　objects,　since　horizontal　and　vertical　convolutional　kernels　have　good　contour　preservation　advantages,　but　also　greatly　reduce　the　computational　complexity　of　the　proposed　model.　The　experiments　on　two　public　VHR　CD　datasets　demonstrate　that　the　proposed　network　can　provide　higher　detection　accuracy　and　requires　smaller　memory　usage　than　state-of-the-art　networks.