Classification of hyperspectral remote sensing data is more challenging than multispectral
remote sensing data because of the enormous amount of information available in the many …

Unlike conventional hyperspectral image (HSI) classification in general scenes, agricultural
HSI classification poses greater challenges due to the increased occurrence of “same …

Supervised Learning (SL) celebrates a lot of research topics in machine learning (ML) and
provides a large number of applications in multimedia. The effectiveness and performance …

Restricted by the associated factors to spatial resolution in remote sensing, mixed pixels and
relative pure pixels may both exist in hyperspectral images. In this paper, Kernel Archetypal …

Feature/band selection is a common technique to overcome the “curse of dimensionality”
posed by the high dimensionality of hyperspectral imagery. When the image is characterized …

In this paper, the Semi-Supervised Subclass Support Vector Data Description is presented,
a method that operates in both the supervised and the semi-supervised One-class …

Statistical monitoring of advanced production processes is becoming increasingly
challenging due to the large number of key performance variables that characterize a …

Support Vector Data Description (SVDD) is a machine learning technique used for single
class classification and outlier detection. SVDD formulation with kernel function provides a …

Accurate urban land use/cover monitoring is an essential step towards a sustainable future.
As a key part of the classification process, the characteristics of reference data can …

To improve the diagnosis accuracy of analog circuit, this paper presents a second map
support vector data description (SM-SVDD) method, which uses an anomalous and close …