Results 201 to 210 of about 9,839 (245)

LFP-LOC: an LFP power-based method for validating the anatomical placement of high-density neural probes in rodents. [PDF]

Front Neurosci
Perna A, Adamo R, Vincenzi M, Angotzi GN, Ribeiro JF, Berdondini L.   +5 more
europepmc   +1 more source

Design and Development of an Automated Pipeline for Medical Hyperspectral Image Acquisition, Processing, and Fusion. [PDF]

J Imaging
Wühler F, Häußermann TM, Rache A, van Marwick B, Wängler C, Reichwald J, Rädle M.   +6 more
europepmc   +1 more source

Deep Architectures Fail to Generalize: A Lightweight Alternative for Agricultural Domain Transfer in Hyperspectral Images. [PDF]

Sensors (Basel)
Pankajakshan P, Padmasanan A, Sundar S.
europepmc   +1 more source

Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging. [PDF]

Imaging Neurosci (Camb)
Codina T, Blankertz B, von Lühmann A.
europepmc   +1 more source

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Stochastic gate-based autoencoder for unsupervised hyperspectral band selection

Pattern Recognition, 2022
Lizhi Wang, Hua Huang, He Sun
exaly   +2 more sources

Unsupervised Hyperspectral Band Selection by Dominant Set Extraction

IEEE Transactions on Geoscience and Remote Sensing, 2016
Unsupervised 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.
Feifei Xu, Zhongqin Bi, Jingsheng Lei
exaly   +2 more sources

Unsupervised Band Selection by Integrating the Overall Accuracy and Redundancy

IEEE Geoscience and Remote Sensing Letters, 2015
Band 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
exaly   +2 more sources

Unsupervised Hyperspectral Image Band Selection via Column Subset Selection

IEEE Geoscience and Remote Sensing Letters, 2015
In this letter, we proposed a novel band selection algorithm for hyperspectral images (HSIs) based on column subset selection. The main idea of the proposed algorithm comes from the column subset selection problem in numerical linear algebra. It selects a group of bands, which maximizes the volume of the selected subset of columns.
Maoguo Gong, Mingyang Zhang, Chi Wang
exaly   +2 more sources

Unsupervised Hyperspectral Band Selection Based on Hypergraph Spectral Clustering

IEEE Geoscience and Remote Sensing Letters, 2022
Hyperspectral images can provide spectral characteristics related to the physical properties of different materials, which arouses great interest in many fields. Band selection (BS) could effectively solve the problem of high dimensions and redundant information of HSI data.
Jingyu Wang 0002, Hongmei Wang, Zhenyu Ma, Lin Wang 0040, Qi Wang 0009, Xuelong Li 0001   +5 more
openaire   +1 more source

Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis

IEEE Geoscience and Remote Sensing Letters, 2008
Band 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
exaly   +2 more sources