Results 201 to 210 of about 4,953 (252)

Detection of Frost-Damaged Maize Kernels for Food Quality Screening Using Hyperspectral Imaging and 2D-COS-Assisted Deep Learning. [PDF]

Food Sci Nutr
Nan J, Wang D, Wang H, Hu C, Li J, Zhang W, Li F, Han J.   +7 more
europepmc   +1 more source

Hyperspectral data-driven corn nitrogen monitoring: application and interpretability analysis of multi-source feature optimization and stacked ensemble learning methods. [PDF]

Front Plant Sci
Kong H, Gu Y, Zhao P, Zheng Y, Tian L, Dong Q.   +5 more
europepmc   +1 more source

Hyperspectral Image Classification Based on an Improved Octopus Optimization Algorithm. [PDF]

Biomimetics (Basel)
Xu Y, Jiang L, Zhang Y.
europepmc   +1 more source

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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 ...
Qian Du, Weiwei Sun
exaly   +2 more sources

Hyperspectral band selection for human detection

2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012
Human detection based on spectral information is required for various applications, e.g. surveillance, tracking and missing person investigation. In practice, spectral human detection encounters the inherent challenge, i.e. multiple targets detection based on a limited number of spectral bands, because (1) there is a great variety in spectral profiles ...
Kuniaki Uto, Yukio Kosugi, Toru Murase, Sigenori Takagishi   +3 more
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Morphological Band Selection for Hyperspectral Imagery

IEEE Geoscience and Remote Sensing Letters, 2018
In this letter, a novel morphological band selection method is proposed to obtain the most representative bands from hyperspectral image (HSI) in an unsupervised manner. In order to sufficiently process the HSI, we propose to use only a small set of data instead of using the original full data.
Wang, Jingyu, Wang, Xianyu, Zhang, Ke, Madani, Kurosh, Sabourin, Christophe   +4 more
openaire   +1 more source

Fast Band Selection for Hyperspectral Imagery

2011 IEEE 17th International Conference on Parallel and Distributed Systems, 2011
Band selection is a common technique for dimensionality reduction of hyperspectral imagery. When the desired object information is unknown, an unsupervised band selection approach is employed to select the most distinctive and informative bands. However, it may be time-consuming for unsupervised band selection methods that need to take all pixels into ...
He Yang, Qian Du 0001
openaire   +1 more source

Dynamic band selection for hyperspectral imagery

2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
This paper presents a new BS, called dynamic BS (DBS) which revolutionizes the commonly used BS by considering the number of bands to be selected, p as a variable which varies with criterion used for BS and different applications. Its idea is derived from information theory where it assumes that signal sources are considered as source alphabets with ...
Keng-Hao Liu, Chein-I Chang
openaire   +1 more source