2018/02/27 by Ramanarayan Mohanty, S L Happy, Aurobinda Routray
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Benchmark (surveying) #Computer science #Data point #Dimensionality reduction #Discriminative model #Face and Expression Recognition #Geography #Hyperspectral imaging #Image Retrieval and Classification Techniques #Linear map #Mathematics #Pattern recognition (psychology) #Projection (relational algebra) #Remote-Sensing Image Classification #Transformation (genetics) #Transformation matrix #cs.LG #stat.ML
paper · pdf · doi:10.1109/lgrs.2018.2804888
published as IEEE Geoscience and Remote Sensing Letters, Volume-15, Issue-4, Pages-582-586, 27 February 2018
openalex publication_date 2018/02/27 · openalex created_date 2018/03/29 · arxiv created 2018/07/07 · arxiv updated 2018/07/10 · openalex updated_date 2026/08/05
The lack of proper class discrimination among the hyperspectral (HS) data points poses a potential challenge in HS classification. To address this issue, this letter 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.