Modified Schroedinger Eigenmap Projections Algorithm for Hyperspectral Imagery Classification

Schroedinger Eigenmap Projections (SEP) is a well-established dimensionality reduction technique for hyperspectral images; it is the linear approximation of Schroedinger Eigenmap (SE) method indeed. The SEP approach is based on Locality Preserving Projections (LPP) algorithm in which the adjacency graph is created in advance without taking in consideration the number of data points in ground objects. This is can negatively affect on hyperspectral reduction and classification process. In this paper, to resolve the problem, we adopted a variant of LPP termed modified LPP (MLPP) instead of original LPP. MLPP adopts an adaptive strategy to create the adjacency graph in which the number of neighbors for each data point can be chosen adaptively. The proposed feature extraction technique uses the Schroedinger operator in the MLPP framework. Indian Pines scene was used for this study. The classification results show effective classification accuracies according to the SEP and other dimensionality reduction methods.

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