Results 1 to 10 of about 1,511 (182)

A Supervised Method for Nonlinear Hyperspectral Unmixing [PDF]

Remote Sensing, 2019
Due to the complex interaction of light with the Earth’s surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occurring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data.
, Richard Gloaguen, Isabel Cecilia Contreras Acosta   +2 more
exaly   +5 more sources

Spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints [PDF]

Scientific Reports
Hyperspectral sparse unmixing, an image processing technique, leverages a spectral library enriched with endmember spectral information as a prerequisite.
Chenguang Xu
doaj   +2 more sources

Blind and endmember guided autoencoder model for unmixing the absorbance spectra of phytoplankton pigments [PDF]

Scientific Reports
Hyperspectral sensing of phytoplankton, free-living microscopic photosynthetic organisms, offers a comprehensive and scalable method for assessing water quality and monitoring changes in aquatic ecosystems.
Pritish Naik, Ilkka Pölönen, Pauliina Salmi   +2 more
doaj   +2 more sources

Endmember-Free Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Unmixing networks for hyperspectral images (HSIs) often need to be redesigned for each sensor and initialized with endmember-estimation algorithms, which limits cross-scene generalization.
Baisen Liu, Longfei Hou, Weiming Zheng, Nazik Alturki, Jamel Baili, Weili Kong, Jiaming Pei   +6 more
doaj   +2 more sources

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders [PDF]

Proceedings of the National Academy of Sciences of the United States of America
Dimitar Georgiev, Mauricio Barahona, Simon Vilms Pedersen   +2 more
exaly   +2 more sources

Autoencoder-Based Hyperspectral Unmixing with Simultaneous Number-of-Endmembers Estimation [PDF]

Sensors
Maher Benismail, Ouiem Bchir
exaly   +2 more sources

Robust Dual Spatial Weighted Sparse Unmixing for Remotely Sensed Hyperspectral Imagery

Remote Sensing, 2023
Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries.
Chengzhi Deng, Yonggang Chen, Shaoquan Zhang, Fan Li, Pengfei Lai, Dingli Su, Min Hu, Shengqian Wang   +7 more
doaj   +1 more source

Robust Hyperspectral Unmixing with Practical Learning-Based Hyperspectral Image Denoising

Remote Sensing, 2023
The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral unmixing algorithms.
Risheng Huang, Xiaorun Li, Yiming Fang, Zeyu Cao, Chaoqun Xia   +4 more
doaj   +1 more source

Graph Attention Convolutional Autoencoder-Based Unsupervised Nonlinear Unmixing for Hyperspectral Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Hyperspectral unmixing has received increasing attention as a technique for estimating endmember spectra and fractional abundances of land covers. Encoding high-dimensional hyperspectral data into a low-dimensional latent space to generate reasonable ...
Danni Jin, Bin Yang
doaj   +1 more source

An Improved Hyperspectral Unmixing Approach Based on a Spatial–Spectral Adaptive Nonlinear Unmixing Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
The autoencoder (AE) framework is usually adopted as a baseline network for hyperspectral unmixing. Totally an AE performs well in hyperspectral unmixing through automatically learning low-dimensional embedding and reconstructing data.
Xiao Chen, Xianfeng Zhang, Miao Ren, Bo Zhou, Ziyuan Feng, Junyi Cheng   +5 more
doaj   +1 more source