The advancement in science and technology has led to a substantial amount of data from
various fields of agriculture to be incremented in the public domain. Hence a desideratum …

In this article, a comprehensive review of the state-of-art graph-based learning methods for
classification of the hyperspectral images (HSI) is provided, including a spectral information …

The development in technology and science has contributed to a vast volume of data from
various agrarian fields to be aggregated in the public domain. Predicting the crop yield …

Classification is a common objective when analyzing hyperspectral images, where each
pixel is assigned to a predefined label. Deep learning-based algorithms have been …

Abstract Graph-based Semi-Supervised Learning (GSSL) methods aim to classify unlabeled
data by learning the graph structure and labeled data jointly. In this work, we propose a …

Most hyperspectral image (HSI) data exist in the form of tensor; the tensor representation
preserves the potential spatial–spectral structure information compared with the vector …

Advanced Hyperspectral image sensors can capture high-resolution land cover images.
Many supervised Machine learning (ML) and Deep learning (DL) algorithms succeeded in …

Constructing a good graph that can capture intrinsic data structures is critical for graph-
based semi-supervised learning methods, which are widely applied for hyperspectral image …

In this article, the terrain classifications of polarimetric synthetic aperture radar (PolSAR)
images are studied. A novel semi-supervised method based on improved Tri-training …

The development in science and technical intelligence has incited to represent an extensive
amount ofdata from various fields of agriculture. Therefore an objective rises up for the …