Sparse　representation　classification　(SRC)　is　becoming　a　promising　tool　for　hyperspectral　image　(HSI)　classification,　where　the　Euclidean　spectral　distance　(ESD)　is　widely　used　to　reflect　the　fidelity　between　the　original　and　reconstructed　signals.　In　this　paper,　a　generalized　model　is　proposed　to　extend　SRC　by　characterizing　the　spectral　fidelity　with　flexible　similarity　measures.　To　validate　the　flexibility,　several　typical　similarity　measures—the　spectral　angle　similarity　(SAS),　spectral　information　divergence　(SID),　the　structural　similarity　index　measure　(SSIM),　and　the　ESD—are　included　in　the　generalized　model.　Furthermore,　a　general　solution　based　on　a　gradient　descent　technique　is　used　to　solve　the　nonlinear　optimization　problem　formulated　by　the　flexible　similarity　measures.　To　test　the　generalized　model,　two　actual　HSIs　were　used,　and　the　experimental　results　confirm　the　ability　of　the　proposed　model　to　accommodate　the　various　spectral　similarity　measures.　Performance　comparisons　with　the　ESD,　SAS,　SID,　and　SSIM　criteria　were　also　conducted,　and　the　results　consistently　show　the　advantages　of　the　generalized　model　for　HSI　classification　in　terms　of　overall　accuracy　and　kappa　coefficient.　©　2016　Elsevier　B.V.