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 …

Feature extraction (FE) is a crucial research area in hyperspectral image (HSI) processing.
Recently, due to the powerful ability of deep learning (DL) to extract spatial and spectral …

For hyperspectral target detection, it is usually the case that only part of the targets pixels
can be used as target signatures, so can we use them to construct the most proper …

This paper proposes a spectral-spatial feature learning (SSFL) method to obtain robust
features of hyperspectral images (HSIs). It combines the spectral feature learning and spatial …

In recent publications, we have presented a data-driven approach to representing the
nonlinear structure of hyperspectral imagery using manifold coordinates. The approach …

Remote sensing data processing deals with real-life applications with great societal values.
For instance urban monitoring, fire detection or flood prediction from remotely sensed …

A novel co-regularization framework for active learning is proposed for hyperspectral image
classification. The first regularizer explores the intrinsic multi-view information embedded in …

Dimensionality reduction (DR) is a widely used technique to address the curse of
dimensionality when high-dimensional remotely sensed data, such as multi-temporal or …

In this paper, anomaly detection in hyperspectral images is investigated using robust locally
linear embedding (RLLE) for dimensionality reduction in conjunction with the RX anomaly …

The accurate classification of hyperspectral images is an important task for many practical
applications. In this paper, a new method for hyperspectral image classification is proposed …