Results 31 to 40 of about 3,420,393 (309)

Bathymetric-Based Band Selection Method for Hyperspectral Underwater Target Detection

Remote Sensing, 2021
Band selection has imposed great impacts on hyperspectral image processing in recent years. Unfortunately, few existing methods are proposed for hyperspectral underwater target detection (HUTD).
Jiahao Qi, Zhiqiang Gong, Aihuan Yao, Xingyue Liu, Yongqian Li, Yichuang Zhang, Ping Zhong   +6 more
doaj   +1 more source

An improved cuckoo search-based adaptive band selection for hyperspectral image classification

European Journal of Remote Sensing, 2020
The information in hyperspectral images usually has a strong correlation, a large number of bands, which lead to the “curse of dimensionality”. So, band selection is usually used to address this issue. However, problems remain for band selection, such as
Shiwei Shao
doaj   +1 more source

Hyper-Graph Regularized Kernel Subspace Clustering for Band Selection of Hyperspectral Image

IEEE Access, 2020
Band selection is an effective way to deal with the problem of the Hughes phenomenon and high computation complexity in hyperspectral image (HSI) processing. Based on the hypothesis that all the pixels are sampled from the union of subspaces, many robust
Meng Zeng, Bin Ning, Chunyang Hu, Qiong Gu, Yaoming Cai, Shuijia Li   +5 more
doaj   +1 more source

Hyperspectral Feature Selection for SOM Prediction Using Deep Reinforcement Learning and Multiple Subset Evaluation Strategies

Remote Sensing, 2022
It has been widely certified that hyperspectral images can be effectively used to monitor soil organic matter (SOM). Though numerous bands reveal more details in spectral features, information redundancy and noise interference also come accordingly.
Linya Zhao, Kun Tan, Xue Wang, Jianwei Ding, Zhaoxian Liu, Huilin Ma, Bo Han   +6 more
doaj   +1 more source

Contribution of band selection and fusion for hyperspectral classification [PDF]

2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014
For some specific land cover classification problems, it may be interesting to design superspectral camera systems with reduced numbers of bands (∼ 20) and optimized band widths. This paper assesses the contribution of band selection and band fusion processes separately and jointly for dimensionality reduction.
Nesrine Chehata, Arnaud Le Bris, Safa Najjar   +2 more
openaire   +2 more sources

Correlation-Guided Ensemble Clustering for Hyperspectral Band Selection

Remote Sensing, 2022
Hyperspectral band selection is a commonly used technique to alleviate the curse of dimensionality. Recently, clustering-based methods have attracted much attention for their effectiveness in selecting informative and representative bands.
Wenguang Wang, Wenhong Wang, Hongfu Liu
doaj   +1 more source

EXTRACTION OF OPTIMAL SPECTRAL BANDS USING HIERARCHICAL BAND MERGING OUT OF HYPERSPECTRAL DATA [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
Spectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspectral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land ...
A. Le Bris, N. Chehata, X. Briottet, N. Paparoditis   +3 more
doaj   +1 more source

LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification [PDF]

IEEE Transactions on Geoscience and Remote Sensing
The fusion of hyperspectral and light detection and range (LiDAR) data has been an active research topic. Existing fusion methods have ignored the high-dimensionality and redundancy challenges in hyperspectral images (HSIs), despite that band selection ...
Judy X. Yang, Jun Zhou, Jing Wang, Hui Tian, Alan Wee-Chung Liew   +4 more
semanticscholar   +1 more source

Similarity-Based Hyperspectral Band Selection Using Deep Reinforcement Learning

, 2022
The main goal of hyperspectral band selection is to select a subset of bands to reduce the redundancy in hyperspectral images. Deep reinforcement learning was recently introduced for this task, which adopts a deep Q-network as the agent and information ...
Tuxworth, Gervase, Zhou, Jun, Bao, Dong
core   +1 more source

Unsupervised Band Selection of Hyperspectral Images via Multi-Dictionary Sparse Representation

IEEE Access, 2018
Band selection is a direct and effective method to reduce the spectral dimension, which is one of popular topics in hyperspectral remote sensing. Recently, a number of methods were proposed to deal with the band selection problem.
Fei Li, Pingping Zhang, Lu Huchuan
doaj   +1 more source