A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images
This work addresses classification accuracy issues for hyperspectral imaging applications, but it appears incremental as it builds on existing methods with a novel transformation.
The paper tackled the challenge of poor class discrimination in hyperspectral image classification by proposing a geometry-aware linear transformation that maps data to a more discriminative space, resulting in improved classification and dimensionality reduction performance on three benchmark datasets.
The lack of proper class discrimination among the Hyperspectral (HS) data points poses a potential challenge in HS classification. To address this issue, this paper proposes an optimal geometry-aware transformation for enhancing the classification accuracy. The underlying idea of this method is to obtain a linear projection matrix by solving a nonlinear objective function based on the intrinsic geometrical structure of the data. The objective function is constructed to quantify the discrimination between the points from dissimilar classes on the projected data space. Then the obtained projection matrix is used to linearly map the data to more discriminative space. The effectiveness of the proposed transformation is illustrated with three benchmark real-world HS data sets. The experiments reveal that the classification and dimensionality reduction methods on the projected discriminative space outperform their counterpart in the original space.