Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification
This work addresses misclassification issues in hyperspectral image analysis, which is important for applications like remote sensing, but it is incremental as it builds on existing spectral feature methods.
The paper tackled the problem of hyperspectral image classification by leveraging the complementary strengths of spectral magnitude and derivative features to reduce confusion between classes, achieving state-of-the-art results on nine benchmark datasets including WHU-OHS.
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.