The ability to capture fine spectral discriminative information enables hyperspectral images
(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the …

With the rapid growth of Sentinel-1 synthetic aperture radar (SAR) data, how to exploit
Sentinel-1 imagery and achieve effective and robust marine surveillance are crucial …

Despite various approaches proposed to smooth the hyperspectral images (HSIs) before
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …

Data acquired from multichannel sensors are a highly valuable asset to interpret the
environment for a variety of remote sensing applications. However, low spatial resolution is …

Feature extraction is of high importance for effective data classification in hyperspectral
imaging (HSI). Considering the high correlation among band images, spectral-domain …

Hyperspectral pansharpening aims to synthesize a low-resolution hyperspectral image (LR-
HSI) with a registered panchromatic (PAN) image to generate an enhanced HSI with high …

Band selection plays an important role in hyperspectral data analysis as it can improve the
performance of data analysis without losing information about the constitution of the …

This paper introduces the beta compositional model (BCM) for hyperspectral unmixing and
four algorithms for unmixing given the BCM. Hyperspectral unmixing estimates the …

The possibility of quantifying iron content in the topsoil of the slopes of the El Hacho
Mountain complex in Southern Spain using imaging spectroscopy is investigated …

Hyperspectral image classification with limited labeled samples is a challenging task and
still an open research issue. In this article, a novel technique is presented to address such …