A Semisupervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images (original) (raw)

Manifold Learning for Classification of Hyperspectral Image Data

2013

Interest in manifold learning for representing the topology of large, high dimensional nonlinear data sets in lower, but still meaningful dimensions for visualization and classification has grown rapidly over the past decade, and particularly in analysis of hyperspectral imagery. High spectral resolution and the typically continuous bands of hyperspectral image (HSI) data enable discrimination between spectrally similar targets of interest, provide capability to estimate within pixel abundances of constituents, and allow direct exploitation of absorption features in predictive models. The spectral response of the narrow bands is often nonlinear and includes the effects of multipath scattering, localized differences in bidirectional reflectance, and non-uniform attenuation that are often exhibited in remote sensing applications [1]. Because of the dense spectral sampling of HSI data, the associated spectral information in many adjacent bands is highly correlated, resulting in much lo...

A Semi-Supervised Reduced-Space Method for Hyperspectral Imaging Segmentation

Journal of Imaging

The development of the hyperspectral remote sensor technology allows the acquisition of images with a very detailed spectral information for each pixel. Because of this, hyperspectral images (HSI) potentially possess larger capabilities in solving many scientific and practical problems in agriculture, biomedical, ecological, geological, hydrological studies. However, their analysis requires developing specialized and fast algorithms for data processing, due the high dimensionality of the data. In this work, we propose a new semi-supervised method for multilabel segmentation of HSI that combines a suitable linear discriminant analysis, a similarity index to compare different spectra, and a random walk based model with a direct label assignment. The user-marked regions are used for the projection of the original high-dimensional feature space to a lower dimensional space, such that the class separation is maximized. This allows to retain in an automatic way the most informative featur...

Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images

2017

In this paper, we propose an L1 normalized graph based dimensionality reduction method for Hyperspectral images, called as ‘L1-Scaling Cut’ (L1-SC). The underlying idea of this method is to generate the optimal projection matrix by retaining the original distribution of the data. Though L2-norm is generally preferred for computation, it is sensitive to noise and outliers. However, L1-norm is robust to them. Therefore, we obtain the optimal projection matrix by maximizing the ratio of between-class dispersion to within-class dispersion using L1- norm. Furthermore, an iterative algorithm is described to solve the optimization problem. The experimental results of the HSI classification confirm the effectiveness of the proposed L1-SC method on both noisy and noiseless data.