Results 81 to 90 of about 23,100 (259)

Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Band selection (BS) identifies key bands from hyperspectral imagery (HSI) for specific downstream tasks, playing a pivotal role in practical applications.
Xiaodi Shang, Chuanyu Cui, Xudong Sun, Xiaopeng Wang, Jiahua Zhang   +4 more
doaj   +1 more source

An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor

Remote Sensing, 2019
A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, so it is necessary to find representative subsets before further analysis.
Qiang Li, Qi Wang, Xuelong Li
doaj   +1 more source

Regularized Sparse Band Selection via Learned Pairwise Agreement

IEEE Access, 2020
Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned Pairwise Agreement (RBS-LPA), was proposed. The
Zhixi Feng, Shuyuan Yang, Xiaolong Wei, Quanwei Gao, Licheng Jiao   +4 more
doaj   +1 more source

Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition

, 2017
Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, and many others. Such complex noise could degrade the quality of the acquired
Leung, Yee, Meng, Deyu, Peng, Jiangjun, Wang, Yao, Zhao, Qian, Zhao, Xi-Le   +5 more
core   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

A Local Potential-Based Clustering Algorithm for Unsupervised Hyperspectral Band Selection

IEEE Access, 2019
Unsupervised band selection plays an increasingly important role in a hyperspectral image (HSI) classification because of inadequate labeling samples.
Zhaokui Li, Lin Huang, Jinrong He, Cuiwei Liu, Xiangbin Shi, Deyuan Zhang   +5 more
doaj   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Physically Based Predictive Modelling of Archaeological Proxies Using Cropmarks

Archaeological Prospection, EarlyView.
ABSTRACT Cropmarks, as archaeological proxies, offer a valuable means of detecting buried sites through remote sensing. Yet, the scalability of such methods across varied archaeological contexts remains underexplored, and AI‐based modelling approaches are still in early stages.
Elias Gravanis, Athos Agapiou
wiley   +1 more source

Convolutional-Neural-Network-Based Onboard Band Selection for Hyperspectral Data With Coarse Band-to-Band Alignment

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Band selection is a key strategy to address the challenges of managing large hyperspectral datasets and reduce the dimensionality problem associated with the simultaneous analysis of hundreds of spectral bands.
David Llaveria Godoy, Nicolas Longepe, Gabriele Meoni, Roberto del Prete, Adriano Camps   +4 more
doaj   +1 more source

Hyperspectral Band Selection From Statistical Wavelet Models

IEEE Transactions on Geoscience and Remote Sensing, 2017
High spectral resolution brings hyperspectral images with large amounts of information, which makes these images more useful in many applications than images obtained from traditional multispectral scanners with low spectral resolution. However, the high data dimensionality of hyperspectral images increases the burden on data computation, storage, and ...
Siwei Feng, Yuki Itoh, Mario Parente, Marco F. Duarte   +3 more
openaire   +1 more source