This article gives a survey of state-of-the-art methods for processing remotely sensed big
data and thoroughly investigates existing parallel implementations on diverse popular high …

This paper reviews the state-of-the-art representation-based classification and detection
approaches for hyperspectral remote sensing imagery, including sparse representation …

As one of the fundamental research topics in remote sensing image analysis, hyperspectral
image (HSI) classification has been extensively studied so far. However, how to …

The large data volume and high algorithm complexity of hyperspectral image (HSI) problems
have posed big challenges for efficient classification of massive HSI data repositories …

Linear discriminative analysis (LDA) is an effective feature extraction method for
hyperspectral image (HSI) classification. Most of the existing LDA-related methods are …

To enhance spectral unmixing performance, a large number of algorithms have
simultaneously investigated spatial and spectral information in hyperspectral images …

Effective classification algorithm is a key to extracting interesting and useful information from
hyperspectral images (HSI). Many researchers have worked on develo** effective …

A key challenge in hyperspectral image (HSI) classification is how to effectively utilize the
spectral and spatial information of limited labeled training samples in the data set. In this …

The fusion of spatial and spectral information in hyperspectral images (HSIs) is useful for
improving the classification accuracy. However, this approach usually results in features of …

Various brain tumors are very harmful to human health, and their incidence rate has risen
gradually in recent years. Magnetic resonance imaging is widely used in the evaluation and …