Multispectral remote sensing images are the primary source in the land use and land cover
(LULC) monitoring. This is achieved by LULC classification and LULC change detection …

Stroke is one of the prominent causes of death in the recent days. The existence of
susceptible plaque in the carotid artery can be used in ascertaining the possibilities of …

Classification is an important technique for remotely sensed hyperspectral image (HSI)
exploitation. Often, the presence of wrong (noisy) labels presents a drawback for accurate …

The “noisy label” problem is one of the major challenges in hyperspectral image (HSI)
classification. In order to address this problem, a spatial density peak (SDP) clustering …

In this paper, a novel method is introduced to detect and correct mislabeled training samples
for hyperspectral image classification. First, domain transform recursive filtering-based …

Mislabeled training samples may have a negative effect on the performance of hyperspectral
image classification. In order to solve this problem, a new density peak (DP) clustering …

Recently spectral–spatial information based algorithms are gaining more attention because
of its robustness, accuracy and efficiency. In this paper, an SVM based classification method …

In the field of hyperspectral image classification, a widely used way for objective
performance evaluation of different classification methods is calculating three accuracy …

In hyperspectral image (HSI) classification, the performance of supervised learning tends to
be affected by priori knowledge, ie, quantity and quality of samples. However, it is inevitable …

Classification of hyperspectral remote sensing images is key to extract abundant
information. The researchers are focusing on the development of algorithms for accurate …