For the HSI classification, the recently introduced nearest regularized subspace (NRS)
classifier outperform the sparse representation-based classification (SRC) and collaborative …

Nearest regularized subspace (NRS) has been recently proposed for hyperspectral image
(HSI) classification. The NRS outperforms both collaborative representation classification …

The k-nearest neighbor (k-NN) method relies on Euclidean distance as a classification
measure to obtain the labels of the test samples. Recently, many studies show that joint …

Hyperspectral images (HSI) contains extremely rich spectral and spatial information that
offers great potential to discriminate between various land cover classes. The inherent high …

Hyperspectral images (HSIs) captured a detail range of electromagnetic spectrum from
visible to near to infrared to each pixel. Due to variability of spectral data and lack of labeled …

Various machine learning and deep learning techniques have been proposed for
classification purposes in the case of hyperspectral imaging. Among all the machine …

In recent years, more and more deep learning frameworks are being applied to
hyperspectral image classification tasks and have achieved great results. However, the …

Mixed pixels problem has significant effects on the application of remote sensing images.
Spectral unmixing analysis has been extensively used to solve mixed pixels in hyperspectral …

Abstract Hyperspectral Image Classification represents a challenge because of their high
number of bands, where each band represents a random variable in the classification …

Hyperspectral imaging has gained much popularity as a field of research in recent years. For
the classification of hyperspectral images the main challenge that comes across is to deal …