Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …

In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …

Recent advances in spectral-spatial classification of hyperspectral images are presented in
this paper. Several techniques are investigated for combining both spatial and spectral …

Feature selection is viewed as an important preprocessing step for pattern recognition,
machine learning, and data mining. Neighborhood is one of the most important concepts in …

The primary goal of pattern recognition is supervised or unsupervised classification. Among
the various frameworks in which pattern recognition has been traditionally formulated, the …

This paper addresses the problem of the classification of hyperspectral remote sensing
images by support vector machines (SVMs). First, we propose a theoretical discussion and …

Image‐based computer aided diagnosis systems have significant potential for screening
and early detection of malignant melanoma. We review the state of the art in these systems …

Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth
of the research conducted over the years in the Remote Sensing Signal and Image …

Classification of hyperspectral data with high spatial resolution from urban areas is
investigated. A method based on mathematical morphology for preprocessing of the …

A method is proposed for the classification of urban hyperspectral data with high spatial
resolution. The approach is an extension of previous approaches and uses both the spatial …