Results 101 to 110 of about 68,035 (215)

An object-based approach to quantity and quality assessment of heathland habitats in the framework of natura 2000 using hyperspectral airborne ahs images [PDF]

, 2010
: Straightforward mapping of detailed heathland habitat patches and their quality using remote sensing is hampered by (1) the intrinsic property of a high heterogeneity in habitat species composition (i.e.
Spanhove, T., Scheunders, Paul, Scheunders, P., Haest, B., Thoonen, G., Bertels, L., Kooistra, L., Thoonen, Guy, Vanden Borre, J., Mücher, C.A., Delalieux, S., Mucher, C. A.   +11 more
core  

Tissue Classification of Breast Cancer by Hyperspectral Unmixing. [PDF]

Cancers (Basel), 2023
Jong LS, Post AL, Veluponnar D, Geldof F, Sterenborg HJCM, Ruers TJM, Dashtbozorg B.   +6 more
europepmc   +1 more source

Illumination invariance and shadow compensation via spectro-polarimetry technique [PDF]

, 2013
A major problem for obtaining target reflectance via hyperspectral imaging systems is the presence of illumination and shadow effects. These factors are common artefacts, especially when dealing with a hyperspectral imaging system that has sensors in the
Jackman, James, Soori, Umair, Hong, Kan, Ibrahim, Izzati, Richardson, Mark A., Yuen, Peter W. T., Chen, Tong   +6 more
core   +1 more source

Classification techniques for hyperspectral remote sensing [PDF]

, 2011
This study concerns with classification techniques in high dimensional space such as that of Hyperspectral Imaging (HSI) data sets, with objectives of understanding the strength and weakness of various classifiers and at the same time to study how ...
Kam, Firmin
core   +3 more sources

Non-Negative Matrix Factorization Based on Smoothing and Sparse Constraints for Hyperspectral Unmixing. [PDF]

Sensors (Basel), 2022
Jia X, Guo B.
europepmc   +1 more source

NMF-SAE: An Interpretable Sparse Autoencoder for Hyperspectral Unmixing

, 2021
Hyperspectral unmixing is an important tool to learn the material constitution and distribution of a scene. Model-based unmixing methods depend on well-designed iterative optimization algorithms, which is usually time consuming.
Jun Zhou, Minchao Ye, Xiong, F, Yuntao Qian, Zhou, J, Jianfeng Lu, Ye, M, Lu, J, Fengchao Xiong, Qian, Y   +9 more
core   +1 more source

Hybrid Hyperspectral Unmixing Using Fusion Mamba and Performer Attention

IEEE Access
Hyperspectral unmixing is emerging as a cutting-edge technology with applications across various areas of remote sensing. Several deep learning models, including transformer-based models, have been developed to improve unmixing accuracy.
M. Sreejam, Agilandeeswari Loganathan
doaj   +1 more source

Spectral Mixture Model Inspired Network Architectures for Hyperspectral Unmixing

, 2020
In many statistical hyperspectral unmixing approaches, the unmixing task is essentially an optimization problem given a defined linear or nonlinear spectral mixture model. However, most of the model inference algorithms require a time-consuming iterative
Qian, Qipeng, Qian, Yuntao, Zhou, Jun, Xiong, Fengchao   +3 more
core   +1 more source

Spectral-Spatial Hyperspectral Unmixing Using Multitask Learning

IEEE Access, 2019
Hyperspectral unmixing is an important and challenging task in the field of remote sensing which arises when the spatial resolution of sensors is insufficient for the separation of spectrally distinct materials.
Burkni Palsson, Johannes R. Sveinsson, Magnus O. Ulfarsson   +2 more
doaj   +1 more source

Transformer for Multitemporal Hyperspectral Image Unmixing

IEEE Transactions on Image Processing
Multitemporal hyperspectral image unmixing (MTHU) holds significant importance in monitoring and analyzing the dynamic changes of surface. However, compared to single-temporal unmixing, the multitemporal approach demands comprehensive consideration of information across different phases, rendering it a greater challenge.
Hang Li, Qiankun Dong, Xueshuo Xie, Xia Xu, Tao Li 0022, Zhenwei Shi 0001   +5 more
openaire   +4 more sources