A　hybrid　method　integrated　wavelet　spectral　feature　with　total　least　square　algorithm　for　improving　abundance　estimation　of　hyper-spectral　mixture　pixels　is　proposed.　The　method　uses　the　wavelet　transform　as　a　pre-processing　step　for　the　spectral　feature　extraction　to　decrease　the　within　end-member　variability,　and　then　utilizes　total　least　square　(TLS)　algorithm　to　capture　the　spectral　variations　between　end-members.　The　hybrid　method　can　take　both　technique　advantages　to　reduce　the　impact　of　spectral　variations　with　different　format.　Consequently,　the　approach　provides　a　potential　ability　to　reduce　and　tackle　within　end-member　variation　inherent　in　real　mixture　pixels,　and　hence　to　improve　abundance　estimation.　Experiment　of　simulating　mixture　spectral　data　is　conducted　to　validate　the　procedures,　and　the　results　demonstrate　that　the　proposed　method　can　reduce　the　abundance　estimation　deviation　over　20%　on　average　in　the　case　of　spectral　end-member　variations,　as　compared　to　that　of　the　original　hyper-spectral　signals　with　least　square　estimation　approach　does.　Comparisons　with　the　decomposition　of　wavelet　based　features　(DWT)　and　total　least　square　have　also　been　implemented,　and　the　experiment　shows　the　hybrid　method　can　also　improve　the　abundance　estimation　by　5%-10%　than　those　of　DWT　and　TLS　do　in　terms　of　average　RMSE.