Spectral unmixing is an important technique for quantitatively analyzing hyperspectral
remote sensing images. Recently, constrained nonnegative matrix factorization (NMF) has …

Abstract Spectral Mixture Analysis is one of the fundamental subjects encountered when
dealing with remotely sensed hyperspectral images. Its goal is to identify constituent …

We propose an unmixing framework for enhancing endmember fraction maps using a
combination of spectral and visible images. The new method, data fusion through spatial …

Hyperspectral imaging is crucial for a variety of land-cover mapping and analyzing tasks.
The available large number of reflected light measurements along a wide range of …

High-quality labelled datasets represent a cornerstone in the development of deep learning
models for land use classification. The high cost of data collection, the inherent errors …

High-quality labelled datasets represent a cornerstone in the development of deep learning
models for land use classification. The high cost of data collection, the inherent errors …

The spectral mixture analysis (SMA) plays a vital role in spectral data analysis and extraction
of subpixel information. However, this technique provides only quantitative information …

We propose a new methodology for enhancing the spatial resolution of unsupervised
classification through a fusion of multispectral and visible images. The new method …

Spectral unmixing is a key tool for a reliable quantitative analysis of remotely sensed data.
The process is used to extract subpixel information by estimating the fractional abundances …