Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images
Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images is a scholarly work by Jun Zhou, published in 2017 in ''IEEE Transactions on Image Processing''. The main subjects of the publication include feature extraction, feature engineering, scale-invariant feature transform, invariant, spatial scale, computer science, computer vision, pattern recognition, hyperspectral imaging, Full spectral imaging, mathematics, digital image processing, scale space, and artificial intelligence. The authors propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions.