Results 21 to 30 of about 3,639,317 (240)

Hyperspectral Image Band Selection Based on CNN Embedded GA (CNNeGA)

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Hyperspectral images (HSIs) are a powerful source of reliable data in various remote sensing applications. But due to the large number of bands, HSI has information redundancy, and methods are often used to reduce the number of spectral bands.
Mohammad Esmaeili, Dariush Abbasi-Moghadam, Alireza Sharifi, Aqil Tariq, Qingting Li   +4 more
doaj   +2 more sources

Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image

IET Computer Vision, 2019
Hyperspectral imaging makes it possible to obtain object information with fine spectral resolution as well as spatial resolution, which is beneficial to a wide array of applications. However, there is a high correlation among the bands in a hyperspectral
Xuefeng Jiang, Lin Zhang, Junrui Liu, Shuying Li   +3 more
doaj   +2 more sources

Hyperspectral Band Selection via Heterogeneous Graph Convolutional Self-Representation Network [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral image (HSI) band selection (BS) plays a crucial role in HSI dimensionality reduction, aiming to identify a representative subset of bands with minimal redundancy. However, conventional BS approaches primarily operate in the Euclidean domain,
Junde Chen, Wenzhao Li, Surendra Maharjan, Hesham El-Askary   +3 more
doaj   +2 more sources

Anomaly‐background separation and particle swarm optimization based band selection for hyperspectral anomaly detection

IET Image Processing
As one of the dimensionality reduction techniques of hyperspectral image (HSI), band selection (BS) does not change the spectral characteristics and physical meaning of HSIs, which is beneficial to the identification and analysis of surface objects ...
Xiaodi Shang, Yiqi Duan, Xiaopeng Wang, Baijia Fu, Xudong Sun   +4 more
doaj   +2 more sources

Efficient Graph Convolutional Self-Representation for Band Selection of Hyperspectral Image

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Hyperspectral image (HSI) band selection (BS) is an important task for HSI dimensionality reduction, whose goal is to select an informative band subset containing less redundancy.
Yaoming Cai, Zijia Zhang, Xiaobo Liu, Zhihua Cai   +3 more
doaj   +1 more source

A restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images

, 2020
With hundreds of spectral bands, the rise of the issue of dimensionality in the classification of hyperspectral images is usually inevitable. In this paper, a restrictive polymorphic ant colony algorithm (RPACA) based band selection algorithm (RPACA-BS ...
Wu, P, Ding, X, Zhang, S, Li, H, Dale, P, Cheng, S, Liu, L   +6 more
core   +1 more source

Non-Parametric Spatial Spectral Band Selection methods [PDF]

, 2021
© Cranfield University 2021. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis project is about the development of band selection (BS) techniques for better target detection ...
Moya Torres, Ruben
core   +2 more sources

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

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