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It has always been a hot topic on using hyperspectral data to analyze in-depth crop heavy metal pollution. Some methods were put forward for qualitatively analyzing copper ion (Cu2+) and lead ion (Pb2+) pollution, discriminating the kinds of pollution elements and diagnosing their pollution degrees combined with the feature extraction methods of the higher-order spectral estimation and the gray gradient co-occurrence matrix (GGCM) based on derivative spectral data of the corn leaves stressed by different Cu2+ and Pb2+ concentrations. Firstly, the spectral data of the corn leaves were collected and the Cu2+, Pb2+ contents in the leaves were measured, which the potted corns were cultivated and stressed by different Cu2+ or Pb2+ concentrations. Then, the bisp_rts and bisp_qs matrixes and their bi-spectral 3D graphs were obtained by the bi-spectral estimation (BSE) of differential spectral data sequences of various corn leaves that the BSE was carried out by using the ARMA model parameter method of higher order spectral estimation, so that a corn leaf was analyzed visually and qualitatively to have been polluted or not by Cu2+ and Pb2+, and the kind of the pollution element could be discriminated to be Cu2+ or Pb2+. Finally, the GGCMs were constructed which were corresponded to the bisp_rts or bisp_qs matrixes, the Cu2+ and Pb2+ pollution degrees of corn leaves could be diagnosed by extracting the texture parameter eigenvalues of each GGCM. The experimental results showed that it can not only qualitatively analyze whether the old (O), middle (M) and new (N) leaves of corn were polluted by Cu2+ and Pb2+, but also correctly discriminate the O and M leaves were polluted by which one of the tow element based on the higher-order spectral estimation;the un-uniformities of gray distribution (T1) and energy (T2) eigenvalues of the bisp_rts matrix could reflect the changes of Pb2+ content in O and M leaves, so the T1 and T2 might well diagnose the pollution degree of Pb2+ in O and M leaves, and the small gradient advantage (T3) eigenvalue of the bisp_qs matrix could reflect the changes of Cu2+ content in O and M leaves, so the T3 might well diagnose the pollution degree of Cu2+ in O and M leaves.