  Extracting discriminative information from complex spectral details in
hyperspectral image (HSI) for HSI classification is pivotal. While current
prevailing methods rely on spectral magnitude features, they could cause
confusion in certain classes, resulting in misclassification and decreased
accuracy. We find that the derivative spectrum proves more adept at capturing
concealed information, thereby offering a distinct advantage in separating
these confusion classes. Leveraging the complementarity between spectral
magnitude and derivative features, we propose a Content-driven Spectrum
Complementary Network based on Magnitude-Derivative Dual Encoder, employing
these two features as combined inputs. To fully utilize their complementary
information, we raise a Content-adaptive Point-wise Fusion Module, enabling
adaptive fusion of dual-encoder features in a point-wise selective manner,
contingent upon feature representation. To preserve a rich source of
complementary information while extracting more distinguishable features, we
introduce a Hybrid Disparity-enhancing Loss that enhances the differential
expression of the features from the two branches and increases the inter-class
distance. As a result, our method achieves state-of-the-art results on the
extensive WHU-OHS dataset and eight other benchmark datasets.
