In hyperspectral unmixing, the prevalent model used is the linear mixing model, and a large
variety of techniques based on this model has been proposed to obtain endmembers and …

Training a deep-learning classifier notoriously requires hundreds of labeled samples at
least. Many practical hyperspectral image (HSI) scenarios suffer from a substantial cost …

In this paper, we propose a new framework to visualize hyperspectral images. We present
three goals for such a visualization: 1) preservation of spectral distances; 2) discriminability …

Unmanned aerial vehicles equipped with hyperspectral imagers have emerged as an
essential technology for the characterization of inland water bodies. The high spectral and …

Although hyperspectral remotely sensed data are believed to be nonlinear, they are often
modeled and processed by algorithms assuming that the data are realizations of some …

Few-shot learning (FSL) uses prior knowledge and supervised experience to effectively
classify hyperspectral images (HSIs), thereby reducing the cost of large numbers of labeled …

In the field of agro-business technology, computerization contributes to productivity,
monetary turnover of events along local viability. The interest in tariffs in addition to the …

In this paper we present a new algorithm to transform an RGB color image to a grayscale
image. We propose using nonlinear dimension reduction techniques to map higher …

The Hyperspectral image (HSI) classification is a standard remote sensing task, in which
each image pixel is given a label indicating the physical land-cover on the earth's surface …

Few-shot learning (FSL) uses prior knowledge and supervised experience to effectively
classify hyperspectral images (HSIs), thereby reducing the cost of large numbers of labeled …