Hyperspectral sensors record the reflectance from the Earth's surface over the full range of
solar wavelengths with high spectral resolution. The resulting high-dimensional data contain …

Approaches to combine local manifold learning (LML) and the k-nearest-neighbor (k NN)
classifier are investigated for hyperspectral image classification. Based on supervised LML …

In hyperspectral remote sensing image classification, multiple features, eg, spectral, texture,
and shape features, are employed to represent pixels from different perspectives. It has …

This comprehensive new resource brings you up to date on recent developments in the
classification of hyperspectral images using both spectral and spatial information, including …

Graph construction, which is at the heart of graph-based semisupervised learning (SSL), is
investigated by using manifold learning (ML) approaches. Since each ML method can be …

We study the geometric Whitney problem on how a Riemannian manifold (M, g) can be
constructed to approximate a metric space (X, d_X)(X, d X). This problem is closely related to …

Increased availability of hyperspectral data and greater access to advanced computing have
motivated development of more advanced methods for exploitation of nonlinear …

Let ℳ⊂ ℝ n be a C 2-smooth compact submanifold of dimension d. Assume that the volume
of ℳ is at most V and the reach (ie the normal injectivity radius) of ℳ is greater than τ …

The problems of neglecting spatial features in hyperspectral imagery (HSI) and the high
complexity of Local Tangent Space Alignment (LTSA) still exist in the nonlinear …

In automated remote sensing based image analysis, it is important to consider the multiple
features of a certain pixel, such as the spectral signature, morphological property, and shape …