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 …

Hyperspectral image sensors capture surface reflectance over a range of wavelengths. The
fine spectral information is recorded in terms of hundreds of bands. Hyperspectral image …

Due to the advantages of deep learning, in this paper, a regularized deep feature extraction
(FE) method is presented for hyperspectral image (HSI) classification using a convolutional …

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous
field view in hundreds or thousands of spectral channels with higher spectral resolution than …

This paper introduces the use of single-layer and deep convolutional networks for remote
sensing data analysis. Direct application to multi-and hyperspectral imagery of supervised …

Signal subspace identification is a crucial first step in many hyperspectral processing
algorithms such as target detection, change detection, classification, and unmixing. The …

Airborne hyperspectral remote sensing data with both rich spectral and spatial features can
effectively improve the classification accuracy of vegetation species. However, the spectral …

Hyperspectral image (HSI) classification is an important topic in the community of remote
sensing, which has a wide range of applications in geoscience. Recently, deep learning …

Hyperspectral sensors record the reflectance from the Earth's surface over the full range of
solar wavelengths with high spectral resolution. The resulting high-dimensional data contain …

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 …