A spatial regularized local manifold learning (SR_LML) approach is investigated for dimensionality reduction (DR) of hyperspectral images, where the spatial regularizer constrains the spatial neighbors with high spectral similarity to have similar embedding coordinates. SR_LML requires the processed data to contain good spatial relations, but the requirement may not be satisfied. We solve the problem by dividing the image into small tiles and applying the SR_LML in each of them. In a tile, each pixel is spatially related to some other pixels, and thus good spatial relations of data can be guaranteed and the effect of the spatial regularizer can be maximized. Moreover, an alignment method is applied to merge all the resulted manifolds in different tiles to a globally consistent manifold. The SR_LML performed in small tiles in conjunction with alignment is denoted as SR_LML_TA; it is able to best use of the spatial relations of pixels in the image, and is suitable for large-scale remote sensing scenes. Experiments with airborne visible/infrared imaging spectrometer (AVIRIS), reflective optics system imaging spectrometer (ROSIS), and NSF-funded Center for Airborne Laser Mapping (NCALM) hyperspectral images demonstrated that the manifold coordinates of SR_LML_TA can provide good separation of classes that are difficult to distinguish. The classification results using 1NN classifier indicated SR_LML_TA significantly outperformed the spectral LML, and obtained higher accuracies than several spatial and spectral LML methods.