Results 21 to 30 of about 11,075 (222)

Hyperspectral imaging-based prediction of soluble sugar content in Chinese chestnuts

Frontiers in Forests and Global Change, 2023
Soluble sugars are critical determinants of fruit quality and play a significant role in human nutrition. Chestnuts, rich in soluble sugars, derive their sweetness from them.
Jinhui Yang, Bangchu Gong, Xibing Jiang, Xibing Jiang   +3 more
doaj   +1 more source

An Improved Ant Colony Algorithm for Optimized Band Selection of Hyperspectral Remotely Sensed Imagery

IEEE Access, 2020
The ant colony algorithm (ACA) has been widely used for reducing the dimensionality of hyperspectral remote sensing imagery. However, the ACA suffers from problems of slow convergence and of local optima (caused by loss of population diversity).
Xiaohui Ding, Huapeng Li, Ji Yang, Patricia Dale, Xiangcong Chen, Chunlei Jiang, Shuqing Zhang   +6 more
doaj   +1 more source

Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images

Applied Sciences, 2022
Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even
Loganathan Agilandeeswari, Manoharan Prabukumar, Vaddi Radhesyam, Kumar L. N. Boggavarapu Phaneendra, Alenizi Farhan   +4 more
doaj   +1 more source

Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection

Remote Sensing, 2019
In this paper, a novel unsupervised band selection (BS) criterion based on maximizing representativeness and minimizing redundancy (MRMR) is proposed for selecting a set of informative bands to represent the whole hyperspectral image cube.
Wenqiang Zhang, Xiaorun Li, Liaoying Zhao   +2 more
doaj   +1 more source

Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications

Journal of Imaging, 2020
Despite the numerous band selection (BS) algorithms reported in the field, most if not all have exhibited maximal accuracy when more spectral bands are utilized for classification.
Ruben Moya Torres, Peter W.T. Yuen, Changfeng Yuan, Johathan Piper, Chris McCullough, Peter Godfree   +5 more
doaj   +1 more source

Double Deep Q-Network for Hyperspectral Image Band Selection in Land Cover Classification Applications

Remote Sensing, 2023
Hyperspectral data usually consists of hundreds of narrow spectral bands and provides more detailed spectral characteristics compared to commonly used multispectral data in remote sensing applications.
Hua Yang, Ming Chen, Guowen Wu, Jiali Wang, Yingxi Wang, Zhonghua Hong   +5 more
doaj   +1 more source

Classification Task-Driven Hyperspectral Band Selection via Interpretability From XGBoost

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Band selection (BS) identifies key bands from hyperspectral imagery (HSI) for specific downstream tasks, playing a pivotal role in practical applications.
Xiaodi Shang, Chuanyu Cui, Xudong Sun, Xiaopeng Wang, Jiahua Zhang   +4 more
doaj   +1 more source

Band Priority Index: A Feature Selection Framework for Hyperspectral Imagery

Remote Sensing, 2018
Hyperspectral Band Selection (BS) aims to select a few informative and distinctive bands to represent the whole image cube. In this paper, an unsupervised BS framework named the band priority index (BPI) is proposed.
Wenqiang Zhang, Xiaorun Li, Liaoying Zhao   +2 more
doaj   +1 more source

Liquid biopsy epigenetics: establishing a molecular profile based on cell‐free DNA

Molecular Oncology, EarlyView.
Cell‐free DNA (cfDNA) fragments in plasma from cancer patients carry epigenetic signatures reflecting their cells of origin. These epigenetic features include DNA methylation, nucleosome modifications, and variations in fragmentation. This review describes the biological properties of each feature and explores optimal strategies for harnessing cfDNA ...
Christoffer Trier Maansson, Anders Lade Nielsen, Boe Sandahl Sorensen   +2 more
wiley   +1 more source

Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification

Remote Sensing
Band clustering has been widely used for hyperspectral band selection (BS). However, selecting an appropriate band to represent a band cluster is a key issue.
Chein-I Chang, Yi-Mei Kuo, Kenneth Yeonkong Ma   +2 more
doaj   +1 more source