To　mitigate　the　domain　shift　and　enhance　the　alignment　of　the　spatial-spectral　features,　this　letter　proposes　a　novel　dual-intervention-constrained　mask-adversary　(DICMA)　framework　for　unsupervised　domain　adaptation　(UDA)　of　hyperspectral　image　classification　(HSIC).　Innovatively,　DICMA　integrates　a　generator,　masker,　and　bi-classifier　within　an　adversarial　framework　constrained　by　a　dual　intervention　mechanism.　Specifically,　the　correlation　intervention　module　(CIM)　ensures　the　preservation　and　independence　of　causal　spatial-spectral　variables,　while　the　knowledge　distillation　intervention　module　completes　the　spatial-spectral　generalization　with　constrained　distillation　information.　Besides,　with　the　collaborative　adversarial　training　strategy,　the　proposed　approach　transfers　effective　knowledge　for　spatial-spectral　feature　alignment.　Experimental　results　and　analyses　demonstrate　the　effectiveness　of　the　proposed　DICMA　model,　which　yields　an　accuracy　of　91.15%　in　the　Pavia　University　(PaviaU).　Pavia　Center　(PaviaC).　Our　code　will　be　released　at　https://github.com/Chirsycy/DICMA.