Spectral Unmixing of Hyperspectral Remote Sensing Imagery
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (1 October 2021) | Viewed by 16488
Special Issue Editors
Interests: sparse modelling; classification; clustering; image processing; machine learning; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: statistical image modeling; sparse representation; image restoration and reconstruction; analysis of high-dimensional data; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: image reconstruction; hyperspectral image processing; sparse representation; low rank representation; remote sensing; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; image processing; signal processing; machine learning; mathematical morphology; data fusion; multivariate data analysis; hyperspectral imaging; pansharpening
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
A large number of hyperspectral images (HSIs) are acquired on a daily basis from various Earth observation airborne and spaceborne systems. They measure the objects on the Earth’s surface in hundreds or thousands of spectral channels and thus offer a far better ability to identify the class of land cover materials which are often indistinguishable in the visible domain. This makes HSIs an essential tool in remote sensing finding numerous applications such as in environmental monitoring, precision agriculture, defense and security, etc. However, due to the typical low spatial resolution of HSIs and resulting homogeneously mixed materials, the acquired spectrum of a single pixel may be a combination of spectral signatures of multiple materials, resulting in a mixed spectrum. This makes the processing, analysis, and interpretation of HSIs difficult tasks.
Spectral unmixing addresses this problem by identifying the constituent pure materials, also called endmembers, and their corresponding fractional abundances present in the pixel. Unmixing is an ill-posed inverse problem. Although the spectral unmixing problem has been widely studied over the last fifty years, it remains an active and important research topic in the fields of remote sensing. The goal of this Special Issue of Remote Sensing is to track the latest progress in modeling theories, methodologies, algorithms, and optimizations that are developed for spectral unmixing of hyperspectral remote sensing images. Authors are invited to submit high-quality, original research papers on the topics including, but not limited to, the following:
- Endmember extraction;
- Estimating the number of endmembers;
- Unmixing models (linear or non-linear);
- Spectral unmixing with side information from other data sources;
- Large-scale spectral unmixing models;
- Spectral unmixing with deep learning;
- Applications of spectral unmixing;
- Blind unmixing;
- Unmixing considering spectral variability or outlier
Dr. Shaoguang Huang
Prof. Aleksandra Pizurica
Prof. Hongyan Zhang
Prof. Mauro Dalla Mura
Guest Editors
Manuscript Submission Information
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Keywords
- Endmember extraction
- hyperspectral images
- remote sensing
- spectral unmixing
- inverse problems
- optimization
- machine learning
- deep learning
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