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Compressive-projection principal component analysis (CPPCA) has been developed to provide reconstruction from random projections of hyperspectral pixels and then subsequently extended by coupling it with classification such that the resulting class-dependent CPPCA yielded improved reconstruction performance. This letter provides an even greater integration of spatial and spectral information to further improve reconstruction performance. Specifically, instead of a pixel-based modulo partitioning employed by the original CPPCA sender, this work proposes an alternative block-based modulo partitioning, which preserves local spatial coherence; spatial segmentation is combined with the pixel-wise classification results using a majority voting rule at the receiver. Experimental results demonstrate not only improved reconstruction performance but also better detection of anomalies, as compared with previous approaches.