Spectral unmixing aims at estimating the fractional abundances of a set of pure spectral
materials (endmembers) in each pixel of a hyperspectral image. The wide availability of …

高光谱成像技术具有光谱分辨率高, 图谱合一的独特优势, 是遥感技术发展以来最重大的科技
突破之一. **的高光谱遥感发展与国际基本同步, 在国家和省部级科研项目的支持下 …

The low spatial resolution of hyperspectral images leads to the coexistence of multiple
ground objects in a single pixel (called mixed pixels). A large number of mixed pixels in a …

Remotely sensed hyperspectral images contain several bands (at about adjoining
frequencies) for a similar zone on the surface of the Earth. Hyperspectral unmixing is a …

The nonnegative matrix factorization (NMF) combining with spatial-spectral contextual
information is an important technique for extracting endmembers and abundances of …

In this letter, we propose a sparse unmixing technique using a convolutional neural network
(SUnCNN) for hyperspectral images. SUnCNN is the first deep learning-based technique …

Linear spectral unmixing is an important technique in the analysis of mixed pixels in
hyperspectral images. In recent years, deep learning-based methods have been garnering …

Spectral unmixing is an important task in hyperspectral image (HSI) analysis and
processing. Sparse representation has become a promising semisupervised method for …

An efficient spatial regularization method using superpixel segmentation and graph
Laplacian regularization is proposed for the sparse hyperspectral unmixing method. Since it …

Clustering algorithms play an essential and unique role in classification tasks, especially
when annotated data are unavailable or very scarce. Current clustering approaches in …