The problem of mixed pixels is common in hyperspectral remote sensing image processing and analysis

and the non-negative matrix factorization (NMF) method has been introduced into hyperspectral unmixing. This study proposes a new spatial and spectral preprocessing technique for constrained NMF

thereby making the unmixing process robust to noise. The spatial and spectral preprocessing technique is based on constrained NMF

such as minimum volume constrained NMF and graph-regularized NMF. The unmixing result of constrained NMF is improved by obtaining better endmember candidates from the preprocessing within the spatial and spectral information of the neighborhood. Spatial preprocessing and spatial-spectral preprocessing can both improve the unmixing accuracy of constrained NMF

such as minimum volume constrained NMF and graph-regularized NMF

in five groups of simulation data with different signal-to-noise ratios (SNRs). Spatial preprocessing can enhance the results of constrained NMF in all SNRs

whereas spatial-spectral preprocessing effectively optimizes accuracy

particularly for conditions with low SNR. Real data experiment based on the well-known hyperspectral data captured by the Airborne Visible/Infrared Imaging Spectrometer over Cuprite

Nevada is conducted. Spatial preprocessing improves the unmixing results of constrained NMF in the real data set

and spatial-spectral preprocessing is better than spatial preprocessing. The experiments on both simulated data and real data show that spatial and spectral preprocessing can efficiently improve the unmixing accuracy of constrained NMF

particularly in low SNR condition. This finding indicates robustness to noise with spatial and spectral information. This study introduces spatial and spectral preprocessing into constrained NMF. The unmixing process becomes more robust to noise with constrained NMF by optimizing the endmember candidate using the spatial and spectral information of hyperspectral remote sensing data in complex remote sensing scenes.