Hyperspectral　images　(HSIs)　contain　a　significant　amount　of　spectral　and　spatial　information,　together　with　underlying　redundancy　and　noise,　causing　difficulty　in　HSI-processing　tasks.　State-of-the-art　deep　learning　methods　have　obtained　unprecedented　performance　in　HSI　classification　and　analysis.　However,　these　architectures　face　challenges　of　declining　accuracy　and　lengthy　training　times.　We　propose　a　framework　to　mitigate　these　issues,　composed　of　a　densely　connected　spectral　block　and　preactivation　bottleneck　residual　spatial　block　to　separately　learn　spectral　and　spatial　features.　The　spectral　extraction　block　can　involve　more　spectral　features　with　the　increase　of　the　network　depth,　and　it　solves　the　problem　of　lengthy　training　time　in　traditional　methods,　and　its　densely　connected　structure　achieves　higher　accuracy.　In　the　spatial　extraction　block,　we　use　the　improved　residual　structure　and　introduce　batch　normalization　and　a　parametric　rectified　linear　unit　before　convolutional　layers　to　preactivate　the　network,　reducing　parameters,　and　overfitting.　In　experiments　using　three　classification　approaches　for　comparison,　it　can　be　observed　that　even　compared　to　the　state-of-the-art　method:　spectral-spatial　residual　network　for　HSI　classification,　the　proposed　model　shows　improvements　in　accuracy　of　0.49%,　0.19%,　and　0.35%　on　the　Indian　Pines,　University　of　Pavia,　and　Kennedy　Space　Center　datasets,　respectively.　The　experimental　results　reveal　that　the　model　obtains　better　classification　results　while　effectively　decreasing　the　training　time.　(C)　2020　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE)