  Up to the present, an enormous number of advanced techniques have been
developed to enhance and extract the spatially semantic information in
hyperspectral image processing and analysis. However, locally semantic change,
such as scene composition, relative position between objects, spectral
variability caused by illumination, atmospheric effects, and material mixture,
has been less frequently investigated in modeling spatial information. As a
consequence, identifying the same materials from spatially different scenes or
positions can be difficult. In this paper, we propose a solution to address
this issue by locally extracting invariant features from hyperspectral imagery
(HSI) in both spatial and frequency domains, using a method called invariant
attribute profiles (IAPs). IAPs extract the spatial invariant features by
exploiting isotropic filter banks or convolutional kernels on HSI and spatial
aggregation techniques (e.g., superpixel segmentation) in the Cartesian
coordinate system. Furthermore, they model invariant behaviors (e.g., shift,
rotation) by the means of a continuous histogram of oriented gradients
constructed in a Fourier polar coordinate. This yields a combinatorial
representation of spatial-frequency invariant features with application to HSI
classification. Extensive experiments conducted on three promising
hyperspectral datasets (Houston2013 and Houston2018) demonstrate the
superiority and effectiveness of the proposed IAP method in comparison with
several state-of-the-art profile-related techniques. The codes will be
available from the website:
https://sites.google.com/view/danfeng-hong/data-code.
