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Editorial for Special Issue “Hyperspectral Imaging and Applications”

Remote Sensing, 2019
Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging.
Chein-I Chang, Meiping Song, Junping Zhang, Chao-Cheng Wu   +3 more
doaj   +1 more source

Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images

Remote Sensing, 2017
This paper proposes to use band selection-based dimensionality reduction (BS-DR) technique in addressing a challenging multi-temporal hyperspectral images change detection (HSI-CD) problem. The aim of this work is to analyze and evaluate in detail the CD
Sicong Liu, Qian Du, Xiaohua Tong, Alim Samat, Haiyan Pan, Xiaolong Ma   +5 more
doaj   +1 more source

Hierarchical Clustering Based Band Selection Algorithm for Hyperspectral Face Recognition

IEEE Access, 2019
Hyperspectral face recognition is a small sample size problem, where usually less than four hyperspectral cubes are available as training data. At the same time, hyperspectral face image acquires grayscale images over a series of continuous spectra which
Qidong Chen, Jun Sun, Vasile Palade, Xiaoqian Shi, Li Liu   +4 more
doaj   +1 more source

An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image

Information, 2018
The human face as a biometric trait has been widely used for personal identity verification but it is still a challenging task under uncontrolled conditions.
Yuefeng Zhao, Mengmeng Wu, Liren Zhang, Jingjing Wang, Dongmei Wei   +4 more
doaj   +1 more source

Genetic Algorithm-Based Weighted Constraint Target Band Selection for Hyperspectral Target Detection

Remote Sensing
Hyperspectral imaging (HSI) data pose both opportunities and challenges for target detection due to the high spectral resolution and vast data volume.
Wenbin Chen, Xiyang Zhi, Jianming Hu, Lijian Yu, Qichao Han, Wei Zhang   +5 more
doaj   +1 more source

Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory

Sensors, 2019
Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability ...
Hongmin Gao, Yao Yang, Xiaoke Zhang, Chenming Li, Qi Yang, Yongchang Wang   +5 more
doaj   +1 more source

Research on the Inversion Method of Dust Content on Mining Area Plant Canopies Based on UAV-Borne VNIR Hyperspectral Data

Drones
Monitoring dust on plant canopies around open-pit coal mines is crucial to assessing environmental pollution and developing effective dust suppression strategies.
Yibo Zhao, Shaogang Lei, Xiaotong Han, Yufan Xu, Jianzhu Li, Yating Duan, Shengya Sun   +6 more
doaj   +1 more source

KDANet: a farmland extraction network using band selection and dual attention fusion – a case study of paddy fields and irrigated land in Qingtongxia, China

Geocarto International
Food security is a critical global issue, and accurate monitoring of farmland is essential to address this challenge. However, farmland extraction accuracy is often hindered by variations in land types and scales.
Jianping Pan, Chen Qi, Huijuan Zhang, Yong Hu, Zhaohui Ren, Zong He, Xunxun Wang, Yimeng Li, Yan Wang, Juan Xie   +9 more
doaj   +1 more source

Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification [PDF]

, 2022
Mateus Habermann, Elcio Hideiti Shiguemori, Vincent Frémont   +2 more
openalex   +1 more source

Problem-based band selection for hyperspectral images [PDF]

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
This paper addresses the band selection of a hyperspectral image. Considering a binary classification, we devise a method to choose the more discriminating bands for the separation of the two classes involved, by using a simple algorithm: single-layer neural network. After that, the most discriminative bands are selected, and the resulting reduced data
Habermann, Mateus, Frémont, Vincent, Shiguemori, Elcio   +2 more
openaire   +2 more sources