Results 201 to 210 of about 23,100 (259)

A Model for Identifying the Fermentation Degree of Tieguanyin Oolong Tea Based on RGB Image and Hyperspectral Data. [PDF]

Foods
Huang Y, Chen Y, Li C, Wang T, Zheng C, Zhao J.   +5 more
europepmc   +1 more source

Hyperspectral Imaging-Based Deep Learning Method for Detecting Quarantine Diseases in Apples. [PDF]

Foods
Zhang H, Ye N, Gong J, Xue H, Wang P, Jiao B, Yin L, Qiao X.   +7 more
europepmc   +1 more source

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Hyperspectral Band Selection using Mutual Information

2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2021
Hyperspectral band selection (BS) is an intrinsic problem in hyperspectral image processing. This paper proposes a novel hyperspectral BS method based upon mutual information (MI). The proposed method tries to find the least redundant and most informative band subset from the whole image data cube iteratively.
Neelam Agrawal, Kesari Verma
openaire   +1 more source

Adaptive hyperspectral band selection

SPIE Proceedings, 2005
We present a new technique for adaptive band selection from hyperspectral image cubes for detecting small targets using an anomaly detector. The proposed technique ensures the selection of lowest number of spectral bands using Mahalanobis distance, maximum affordable extra noise variance, and Constant False Alarm Rate (CFAR) anomaly detector threshold.
M. S. Alam, S. Ochilov
openaire   +1 more source

Hyperspectral Band Selection: A Review

IEEE Geoscience and Remote Sensing Magazine, 2019
A hyperspectral imaging sensor collects detailed spectral responses from ground objects using hundreds of narrow bands; this technology is used in many real-world applications. Band selection aims to select a small subset of hyperspectral bands to remove spectral redundancy and reduce computational costs while preserving the significant spectral ...
Weiwei Sun, Qian Du
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Methodology for Hyperspectral Band Selection

Photogrammetric Engineering & Remote Sensing, 2004
While hyperspectral data are very rich in information, processing the hyperspectral data poses several challenges regarding computational requirements, information redundancy removal, relevant information identification, and modeling accuracy.
Peter Bajcsy, Peter Groves
openaire   +1 more source

Hyperspectral Image Band Selection Using Pooling

2020 International Conference Mechatronic Systems and Materials (MSM), 2020
Hyperspectral images contain hundreds of spectral bands. These bands contain abundant information and more often redundant information. This article presents an unsupervised band selection method to choose most significant spectral image bands from hyperspectral datacube which maximize the relavance and minimize redundancy.
Dhanushka C. Liyanage, Robert Hudjakov, Mart Tamre   +2 more
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Band selection based hyperspectral unmixing

2009 IEEE International Workshop on Imaging Systems and Techniques, 2009
Hyperspectral unmixing is the procedure by which the measured spectrum of a mixed pixel is decomposed into a collection of constituent spectra, or endmembers, and their mixing proportions. However, due to the hundreds of spectral bands contained in the hyperspectral imagery, the large amount of data not only increase the computational loads, but also ...
Sen Jia, Zhen Ji, Yuntao Qian
openaire   +1 more source