Providing the kernel function of the local tangent space alignment (LTSA) algorithm, the
generalisation of both LTSA and supervised LTSA (SLTSA) is achieved. Moreover, using the …

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 …

Recently, the sparse representation based classification methods have received particular
attention in the classification of hyperspectral imagery. However, current sparse …

In this paper, we develop an effective classification framework to classify a hyperspectral
image (HSI), which consists of two fundamental components: weighted generalized nearest …

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 …

The paper proposes three novel nonlinear dimensionality reduction methods for
hyperspectral image analysis. The first two methods are based on the principle of preserving …

This article proposes a spectral–spatial method for classification of hyperspectral images
(HSIs) by modifying traditional Auto-Encoder based on Majorization Minimization (MM) …

Fusion of spectral and spatial information is an effective way in improving the accuracy of
hyperspectral image classification. In this paper, a novel spectral–spatial hyperspectral …

Hyperspectral image (HSI) classification is a very active research topic in remote sensing
and has numerous potential applications. This paper presents a simple but effective …

In Hyperspectral image (HSI) classification, combining spectral information with spatial
information has become an efficient measure to obtain good classification results, where …