Results 31 to 40 of about 1,511 (182)

Catadioptric hyperspectral imaging, an unmixing approach [PDF]

IET Computer Vision, 2020
Hyperspectral imaging systems provide dense spectral information on the scene under investigation by collecting data from a high number of contiguous bands of the electromagnetic spectrum. The low spatial resolutions of these sensors frequently give rise to the mixing problem in remote sensing applications.
Didem Ozisik Baskurt, Yalin Bastanlar, Yasemin Yardimci Cetin   +2 more
openaire   +2 more sources

Hyperspectral Unmixing Hierarchies

CoRR
Unmixing reveals the spatial distribution and spectral details of different constituents, called endmembers, in a hyperspectral image. Because unmixing has limited ground truth requirements, can accommodate mixed pixels, and is closely tied to light propagation, it is a uniquely powerful tool for analyzing hyperspectral images.
Joseph L. Garrett, P. S. Vishnu, Pauliina Salmi, Daniela Lupu, Nitesh Kumar Singh, Ion Necoara, Tor Arne Johansen   +6 more
openaire   +2 more sources

ADVANCES IN HYPERSPECTRAL AND MULTISPECTRAL IMAGE FUSION AND SPECTRAL UNMIXING [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
In this work, we jointly process high spectral and high geometric resolution images and exploit their synergies to (a) generate a fused image of high spectral and geometric resolution; and (b) improve (linear) spectral unmixing of hyperspectral ...
C. Lanaras, E. Baltsavias, K. Schindler
doaj   +1 more source

Pixel-Level and Global Similarity-Based Adversarial Autoencoder Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is a critical task in remote sensing, enabling the decomposition of hyperspectral data into their constituent endmembers and abundances.
Wei Tao, Haiyang Zhang, Shan Zeng, Long Wang, Chaoxian Liu, Bing Li   +5 more
doaj   +1 more source

Blind Hyperspectral Unmixing with Enhanced 2DTV Regularization Term

Remote Sensing, 2023
For the problem where the existing hyperspectral unmixing methods do not take full advantage of the correlations and differences between all these bands, resulting in affecting the final unmixing results, we design an enhanced 2DTV (E-2DTV ...
Peng Wang, Xun Shen, Yingying Kong, Xiwang Zhang, Liguo Wang   +4 more
doaj   +1 more source

Identification of an Osteochondral Erosion Process in Gout Driven by Fibrinogen‐Integrin Activation and Local MSU Crystallization

Advanced Science, EarlyView.
Cartilage injury promotes local fibrinogen deposition, which accelerates monosodium urate crystallization and activates integrin‐mediated matrix‐degradation. This self‐amplifying cycle drives gout‐related cartilage erosion. Disrupting fibrinogen deposition or restoring the cartilage barrier could interrupt this vicious cycle.
Hanlin Xu, Simin Bi, Zhechen Liu, Xiaofeng Zhou, Hengwei Qin, Nicola Dalbeth, Minbiao Ji, Yinghui Hua   +7 more
wiley   +1 more source

Hyperspectral Unmixing Network Accounting for Spectral Variability Based on a Modified Scaled and a Perturbed Linear Mixing Model

Remote Sensing, 2023
Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by ...
Ying Cheng, Liaoying Zhao, Shuhan Chen, Xiaorun Li   +3 more
doaj   +1 more source

Super‐Multiplexed Label‐Free Raman Imaging Uncovers Novel Testicular Metabolic Couplings for Residual Body and Spermatogonial Differentiation

Advanced Science, EarlyView.
Super‐multiplexed Label‐free Raman Imaging (SLRI) enables 2D/3D metabolic mapping of intact Drosophila testes. Moving beyond descriptive morphology, it establishes a multidimensional tool for tissue metabolic remodeling, and offers a generalizable platform for complex tissue analysis, with implications extending to development and disease. ABSTRACT The
Jiaxin Li, Xia Chen, Yujuan Qi, Chen Qiao, Qiuru Huang, Dan Zhao, Weiyi Qian, Li Di, Yi Shi, Xiangqi Hu, Yueyue Shao, Kehan Wang, Zhiyu Tang‐Yang, Hanzhi Zang, Yuwen Du, Anran Dong, Jinxiong Feng, Jiayun Chen, Ying Zheng, Xiaofang Tan, Xiaoyu Zhao, Zhonghua Shi, Bo Zheng, Jun Yu   +23 more
wiley   +1 more source

Deep‐Learning‐Driven High‐Fidelity In Vivo Hyperspectral Fluorescence Imaging Under Extreme Photon‐Limited Conditions

Advanced Science, EarlyView.
Highly overlapping fluorescent signals become distinguishable in photon‐limited living organisms via advanced imaging with intelligent reconstruction. The resulting in vivo hyperspectral imaging capability reveals nanoplastic uptake and circulation in live zebrafish, providing a new approach for studying complex biological and environmental processes ...
Renjian Li, Shutao Wu, Kaixiang Li, Zhenyu An, Yuwen Ben, Guiye LI, Sunil Kumar, James Mcginty, Tawfique Hasan, Songnian Fu, Meng Zhang, LingLing Chen   +11 more
wiley   +1 more source

HYPERSPECTRAL IMAGE RESOLUTION ENHANCEMENT BASED ON SPECTRAL UNMIXING AND INFORMATION FUSION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be
J. Bieniarz, D. Cerra, J. Avbelj, P. Reinartz, R. Müller   +4 more
doaj   +1 more source