Results 181 to 190 of about 3,935,477 (223)
Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning
Advanced Intelligent Discovery, EarlyView.Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang +4 more
wiley +1 more source
IEEE Transactions on Geoscience and Remote Sensing, 2016 Band selection is an important preprocessing step for hyperspectral image processing. Many valid criteria have been proposed for band selection, and these criteria model band selection as a single-objective optimization problem. In this paper, a novel multiobjective model is first built for band selection.
Yuan Yuan, Maoguo Gong, Mingyang Zhang
exaly +4 more sources
Novel Hyperbolic Clustering-Based Band Hierarchy (HCBH) for Effective Unsupervised Band Selection of Hyperspectral Images [PDF]
Pattern Recognition, 2022 For dimensionality reduction of HSI, many clustering-based unsupervised band selection (UBS) methods have been proposed due to their superiority of reducing the high redundancy between selected bands.
Jinchang Ren, He Sun
exaly +2 more sources
MIMN-DPP: Maximum-Information and Minimum-Noise Determinantal Point Processes for Unsupervised Hyperspectral Band Selection [PDF]
Pattern Recognition, 2020 Band selection plays an important role in hyperspectral imaging for reducing the data and improving the efficiency of data acquisition and analysis whilst significantly lowering the cost of the imaging system.
Jinchang Ren, Zhijing Yang, Huimin Zhao
exaly +4 more sources
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Stochastic Gate-Based Autoencoder for Unsupervised Hyperspectral Band Selection
Pattern Recognition, 2022Lizhi Wang, He Sun
exaly +2 more sources
Unsupervised Hyperspectral Band Selection by Dominant Set Extraction
IEEE Transactions on Geoscience and Remote Sensing, 2016Unsupervised hyperspectral band selection has been an important topic in hyperspectral imagery. This technique aims at selecting some critical and decisive spectral bands from an original image for compact representation without compromising and distorting the raw information in the relevant spectral bands.
Zhongqin Bi, Jingsheng Lei
exaly +3 more sources
Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis
IEEE Geoscience and Remote Sensing Letters, 2008Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded ...
Qian Du
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Unsupervised Band Selection by Integrating the Overall Accuracy and Redundancy
IEEE Geoscience and Remote Sensing Letters, 2015Band selection is of great significance to alleviate the curse of dimensionality for hyperspectral (HSI) image application. In this letter, we propose a novel unsupervised band selection method for HSI classification. This method integrates both the overall accuracy and redundancy into the band selection process by formulating an optimization model. In
Chenhong Sui, Yong Xie
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Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection
IEEE Transactions on Image Processing, 2017Band selection, as a special case of the feature selection problem, tries to remove redundant bands and select a few important bands to represent the whole image cube. This has attracted much attention, since the selected bands provide discriminative information for further applications and reduce the computational burden.
Yuan Yuan, Xiaoqiang Lu, Xiangtao Zheng
exaly +5 more sources
Marginalized Graph Self-Representation for Unsupervised Hyperspectral Band Selection
IEEE Transactions on Geoscience and Remote Sensing, 2022Unsupervised band selection is an essential step in preprocessing hyperspectral images (HSIs) to select informative bands. Most existing methods exploit the spatial information from the entire HSI while ignoring the difference between diverse homogeneous regions. Moreover, traditional methods utilize the limited size of data for model training that may
Yongshan Zhang +3 more
openaire +2 more sources