Results 1 to 10 of about 9,690 (112)

Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification

Remote Sensing, 2022
A hyperspectral image provides fine details about the scene under analysis, due to its multiple bands. However, the resulting high dimensionality in the feature space may render a classification task unreliable, mainly due to overfitting and the Hughes ...
Mateus Habermann, Elcio Hideiti Shiguemori, Vincent Frémont   +2 more
doaj   +3 more sources

An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images

Remote Sensing, 2023
Band selection (BS) is an efficacious approach to reduce hyperspectral information redundancy while preserving the physical meaning of hyperspectral images (HSIs).
Xiaorun Li, Yufei Liu, Ziqiang Hua, Shuhan Chen   +3 more
doaj   +3 more sources

Interband Consistency-Driven Structural Subspace Clustering for Unsupervised Hyperspectral Band Selection [PDF]

Sensors
In the classification applications of hyperspectral remote sensing images (HSIs), band selection is crucial for mitigating the curse of dimensionality while preserving the intrinsic physical information within HSIs.
Zengke Wang, Wenhong Wang
doaj   +2 more sources

Unsupervised Hyperspectral Band Selection via Multimodal Evolutionary Algorithm and Subspace Decomposition

Sensors, 2023
Unsupervised band selection is an essential task to search for representative bands in hyperspectral dimension reduction. Most of existing studies utilize the inherent attribute of hyperspectral image (HSI) and acquire single optimal band subset while ...
Yunpeng Wei, Huiqiang Hu, Huaxing Xu, Xiaobo Mao   +3 more
doaj   +3 more sources

Unsupervised Hyperspectral Band Selection Using Spectral–Spatial Iterative Greedy Algorithm [PDF]

Sensors
Hyperspectral band selection (BS) is an important technique to reduce data dimensionality for the classification applications of hyperspectral remote sensing images (HSIs). Recently, searching-based BS methods have received increasing attention for their
Xin Yang, Wenhong Wang
doaj   +2 more sources

Rapid FTIR Spectral Fingerprinting of Kidney Allograft Perfusion Fluids Distinguishes DCD from DBD Donors: A Pilot Machine Learning Study [PDF]

Metabolites
Background/Objectives: Rapid, objective phenotyping of donor kidneys is needed to support peri-implant decisions. Label-free Fourier-transform infrared (FTIR) spectroscopy of static cold-storage Celsior® perfusion fluid can discriminate kidneys recovered
Luis Ramalhete, Rúben Araújo, Miguel Bigotte Vieira, Emanuel Vigia, Ana Pena, Sofia Carrelha, Anibal Ferreira, Cecília R. C. Calado   +7 more
doaj   +2 more sources

Unsupervised hyperspectral band selection by combination of unmixing and sequential clustering techniques

European Journal of Remote Sensing, 2019
Selecting the decisive spectral bands is a key issue in unsupervised hyperspectral band selection techniques. These methods are the most popular ways for dimensionality reduction of original data.
Sarra Ikram Benabadji, Moussa Sofiane Karoui, Khelifa Djerriri, Issam Boukerch, Nezha Farhi, Mohammed Amine Bouhlala   +5 more
doaj   +2 more sources

Two-Stage Unsupervised Hyperspectral Band Selection Based on Deep Reinforcement Learning

Remote Sensing
Hyperspectral images are high-dimensional data that capture detailed spectral information across a wide range of wavelengths, enabling the precise identification and analysis of different materials or objects. However, the high dimensionality of the data
Yi Guo, Qianqian Wang, Bingliang Hu, Xueming Qian, Haibo Ye   +4 more
doaj   +3 more sources

Hyperspectral Band Selection via Band Grouping and Adaptive Multi-Graph Constraint

Remote Sensing, 2022
Unsupervised band selection has gained increasing attention recently since massive unlabeled high-dimensional data often need to be processed in the domains of machine learning and data mining.
Mengbo You, Xiancheng Meng, Yishu Wang, Hongyuan Jin, Chunting Zhai, Aihong Yuan   +5 more
doaj   +1 more source

Fractal Autoencoder-Based Unsupervised Hyperspectral Bands Selection for Remote Sensing Land-Cover Classification

Engineering Proceedings, 2023
Band selection is a frequently used dimension reduction technique for hyperspectral images (HSI) to address the “curse of dimensionality” phenomenon in machine learning (ML). This technique identifies and selects a subset of the most important bands from
Sara Benali, Mohammed El Amin Larabi, Dalila Attaf   +2 more
doaj   +1 more source