Results 111 to 120 of about 309,259 (214)

Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model [PDF]

, 2012
This paper studies a nonlinear mixing model for hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian
Altmann, Yoann, Altmann, Yoann; id_orcid, Dobigeon, Nicolas, Tourneret, Jean-Yves   +3 more
core   +1 more source

Fractional Impervious Surface Mapping on Multispectral Images With Visible Shadows via a Bundle-Based Sparse Unmixing Model

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Impervious surface abundance (ISA) is an important indicator for monitoring urbanization and environmental disaster management processes. Commonly used spectral unmixing techniques extract ISA in the form of mixed pixels; however, in previous ...
Yanze Liu, Dacheng Li, Wei Xiong, Qijin Han, Lingling Ma   +4 more
doaj   +1 more source

Blind Hyperspectral Unmixing With Integrated Nonsmooth and CEM Spatial Constraints

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral blind unmixing serves as a key technique in remote sensing image processing. Its core value lies in the ability to extract endmember spectral signatures and invert abundance fractions from mixed pixels without relying on prior endmember ...
Shaoquan Zhang, Kexing Li, Xinyi Zhou, Fan Li, Pengfei Lai, Lianhui Liang, Chengzhi Deng, Shengqian Wang   +7 more
doaj   +1 more source

Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing

, 2015
Hyperspectral unmixing is one of the most important problems in analyzing remote sensing images, which aims to decompose a mixed pixel into a collection of constituent materials named endmembers and their corresponding fractional abundances.
Lu, Xiaoqiang, Yuan, Yuan, Fu, Min
core   +1 more source

Temperature scaling unmixing framework based on convolutional autoencoder

International Journal of Applied Earth Observations and Geoinformation
Hyperspectral unmixing is a key technology in the development of remote sensing applications. However, since both endmembers and abundances are unknown, unmixing is a non-convex problem with a large solution space. To solve this, existing methods usually
Jin Xu, Mingming Xu, Shanwei Liu, Hui Sheng, Zhiru Yang   +4 more
doaj   +1 more source

Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning [PDF]

We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data.
Pisharady, Pramod Kumar, Sotiropoulos, Stamatios N., Lenglet, Christophe, Sapiro, Guillermo, Duarte-Carvajalino, Julio M.   +4 more
core   +1 more source

A Novel Sub-Abundance Map Regularized Sparse Unmixing Framework Based on Dynamic Abundance Subspace Awareness

Mathematics
Sparse unmixing (SU) has become a research hotspot in hyperspectral image (HSI) analysis in recent years due to its interpretable physical mechanisms and engineering practicality.
Kewen Qu, Fangzhou Luo, Huiyang Wang, Wenxing Bao   +3 more
doaj   +1 more source

Impact of sparse representation on the admissible solutions of spectral unmixing by non-negative matrix factorization

, 2015
International audienceSpectral unmixing in the linear mixing model case can be addressed using non-negative matrix factorization (NMF) algorithms.
Moussaoui, Saïd, Kumar, Neeraj, Said Moussaoui, Jerome Idier, Idier, Jérôme, Neeraj Kumar, Brie, David, David Brie   +7 more
core   +1 more source

A Theory-Guided Transformer for Interpretable Hyperspectral Unmixing

Remote Sensing
Hyperspectral unmixing (HU) is fundamental for conducting quantitative analyses in remote sensing, yet existing methods face a persistent tradeoff between model performance and physical interpretability.
Hongyue Cao, Fanlei Meng, Haixin Sun, Xinyu Cui, Dan Shao   +4 more
doaj   +1 more source

Superpixel-Guided Matrix-Valued Kernel Functions for Multiscale Nonlinear Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is a critical challenge in the analysis of hyperspectral remote sensing data. Due to the complex interactions between incident light and materials, which are significantly influenced by the three-dimensional geometry of the scene ...
Xiu Zhao, Meiping Song
doaj   +1 more source