Machine learning offers the potential for effective and efficient classification of remotely
sensed imagery. The strengths of machine learning include the capacity to handle data of …

Biodiesel has the potential to significantly contribute to making transportation fuels more
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …

Extreme learning machine (ELM) has gained increasing interest from various research fields
recently. In this review, we aim to report the current state of the theoretical research and …

Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

Hyperspectral image classification has been a vibrant area of research in recent years.
Given a set of observations, ie, pixel vectors in a hyperspectral image, classification …

Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage
to a country's economy. Floods, being natural disasters, cannot be prevented completely; …

It is of great interest in exploiting texture information for classification of hyperspectral
imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich …

This study proposes a novel learning scheme for the kernel extreme learning machine
(KELM) based on the chaotic moth-flame optimization (CMFO) strategy. In the proposed …

Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI)
classification. However, most SSC methods mainly consider local information, so that some …

Since its introduction, kernel extreme learning machine (KELM) has been widely used in a
number of areas. The parameters in the model have an important influence on the …