Image　classification　is　one　of　the　critical　tasks　in　hyperspectral　remote　sensing.　In　recent　years,　significant　improvement　have　been　achieved　by　various　classification　methods.　However,　mixed　spectral　responses　from　different　ground　materials　still　create　confusions　in　complex　scenes.　In　this　regard,　unmixing　approaches　are　being　successfully　carried　out　to　decompose　mixed　pixels　into　a　collection　of　spectral　signatures.　Considering　the　usefulness　of　these　techniques,　we　propose　to　utilize　the　unmixing　results　as　an　input　to　classifiers　for　better　classification　accuracy.　We　propose　a　novel　band　group　based　structure　preserving　nonnegative　matrix　factorization　(NMF)　method　to　estimate　the　individual　spectral　responses　from　different　materials　within　different　ranges　of　wavelengths.　Then　we　train　a　convolutional　neural　network　(CNN)　with　the　unmixing　results　to　generate　powerful　features　and　eventually　classify　the　data.　This　method　is　evaluated　on　a　new　dataset　and　compared　with　several　state-of-the-art　models,　which　shows　the　promising　potential　of　our　method.