Improved Superpixels Generation Algorithm for Qualified Graph-Based Technique

Abstract

Hyperspectral images (HSIs) represent an important source of information in the remote sensing field. Indeed, HSIs, which collect data in many spectral bands, are more simple interpretable and provide a detailed information about interest areas. However, hyperspectral imaging systems generate a huge amount of redundant data and an important level of noise. Dimensionality reduction is an important task that attempts to reduce dimensionality and remove noise so as to enhance the accuracy of remote sensing applications. The first dimensionality reduction approaches date back to 1970s, and various model-based methods have been proposed since these years. This field has known an increasing attention by the suggestion of graph based models that have yielded promising results. While graph based approaches generate considerable outputs, these models require often an important processing time to handle data. In this work, we aim to reduce the computational burden of a promising graph based method called the Modified Schroedinger Eigenmap Projections (MSEP). In this respect, we suggest an efficient superpixel algorithm, called Improved Simple Linear Iterative Clustering (Improved SLIC), to lessen the heavy computational load of the MSEP method. The proposed approach exploits the superpixels as inputs instead of pixels; and then runs the MSEP algorithm. While respecting the HSIs properties, the proposed scheme illustrates that the MSEP method can be performed with computational efficiency.