Article
Engineering, Multidisciplinary
Wei Zhang, Liang Zhao
Summary: Through analyzing metadata from 4122 literature records, it is observed that in the past decade, research literature related to hyperspectral remote sensing has shown a trend of rapid growth, with more publications from countries like China, the United States, and Germany, mostly coming from university institutions. The research hotspots in hyperspectral remote sensing mainly focus on image classification, algorithm models, spectral resolution/reflectance, and vegetation analysis.
Article
Engineering, Electrical & Electronic
Or Arad, Loran Cheplanov, Yiftah Afgin, Liad Reshef, Roman Brikman, Saker Elatrash, Adrian Stern, Leah Tsror, David J. Bonfil, Iftach Klapp
Summary: A hyperspectral imaging system was developed using a point spectrometer and a double-wedge prism scanner. This system offers an inexpensive alternative to the HS camera, making it suitable for precision agriculture and environmental monitoring.
IEEE SENSORS JOURNAL
(2023)
Article
Chemistry, Multidisciplinary
Silvia E. Zieger, Maria Mosshammer, Michael Kuhl, Klaus Koren
Summary: This study presents a novel optical sensing method using hyperspectral imaging and signal deconvolution for chemical imaging of multiple analytes simultaneously, demonstrating high resolution and flexibility in monitoring different analyte concentrations.
Article
Environmental Sciences
Victor Andres Ayma Quirita, Gilson Alexandre Ostwald Pedro da Costa, Cesar Beltran
Summary: In this work, a distributed version of the N-FINDR endmember extraction algorithm was introduced, which efficiently processes large volumes of hyperspectral data using computer cluster resources. The experimental analysis demonstrated the performance and accuracy of the algorithm in handling large-scale hyperspectral data.
Review
Environmental Sciences
Bowen Chen, Liqin Liu, Zhengxia Zou, Zhenwei Shi
Summary: This paper reviews representative methods for hyperspectral image target detection, and categorizes them into seven categories: hypothesis testing-based methods, spectral angle-based methods, signal decomposition-based methods, constrained energy minimization (CEM)-based methods, kernel-based methods, sparse representation-based methods, and deep learning-based methods. The basic principles, classical algorithms, advantages, limitations, and connections of these methods are comprehensively summarized, and critical comparisons are made on the summarized datasets and evaluation metrics. Furthermore, the future challenges and directions in the area are analyzed.
Article
Remote Sensing
Yanfei Zhong, Xinyu Wang, Shaoyu Wang, Liangpei Zhang
Summary: This paper discusses the recent progress in Chinese spaceborne HRS, including typical satellite systems, data processing, and applications, as well as the future development trends of HRS in China.
GEO-SPATIAL INFORMATION SCIENCE
(2021)
Review
Engineering, Electrical & Electronic
Nour Aburaed, Mohammed Q. Alkhatib, Stephen Marshall, Jaime Zabalza, Hussain Al Ahmad
Summary: Remote sensing technology plays a crucial role in various industrial applications, and hyperspectral imaging (HSI) is an important modality. However, HSI suffers from low spatial resolution compared to multispectral images (MSI). Therefore, super resolution (SR) of HSI has gained attention in the past two decades. This article reviews important SR algorithms, datasets, sensors, and quality metrics in the field from 2002 to 2022, providing a summary of the current state and recommendations for future directions.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Review
Computer Science, Information Systems
Guangsheng Chen, Hailiang Lu, Weitao Zou, Linhui Li, Mahmoud Emam, Xuebin Chen, Weipeng Jing, Jian Wang, Chao Li
Summary: Remote sensing images have been widely used in Earth observation tasks, but a single sensor cannot provide observational images with both high spatial and temporal resolution. The spatiotemporal fusion (STF) method has been proposed to overcome this constraint. Many STF methods have been proposed based on different principles and strategies. A new review is needed to reflect the current research status. This review provides a comprehensive overview of current advances, discusses the basic principles and limitations, and collects recent applications. It also introduces publicly available resources and quantitative metrics, and discusses open problems and challenges for future attention.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Bahadir Celik
Summary: Environmental monitoring studies rely on accurate and up-to-date land use and land cover information, and remote sensing data/techniques are widely used for land cover mapping due to their synoptic view and high temporal resolution capabilities. Linear spectral unmixing technique provides sub-pixel level land cover information, unlike traditional image classification. In this paper, the QLSU plugin, an open source and user-friendly graphical interface tool implemented in QGIS, is introduced for researchers without programming experience to perform linear spectral unmixing on remote sensing imagery. A case study on both real and synthetic images is conducted to demonstrate the plugin's usage and evaluate its results.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Computer Science, Artificial Intelligence
Alexandre Zouaoui, Gedeon Muhawenayo, Behnood Rasti, Jocelyn Chanussot, Julien Mairal
Summary: In this paper, a new algorithm based on archetypal analysis for blind hyperspectral unmixing is introduced. The algorithm represents endmembers as convex combinations of pixels from the original hyperspectral image. A entropic gradient descent strategy is leveraged to provide better solutions and efficient GPU implementations. The proposed method outperforms state-of-the-art matrix factorization and recent deep learning methods in six standard real datasets.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Saeideh Ghanbari Azar, Saeed Meshgini, Soosan Beheshti, Tohid Yousefi Rezaii
Summary: This paper introduces the process of hyperspectral unmixing and methods to address spectral variability, proposing the LMM-SBD method that combines scaling factors and bundle dictionary. By considering spatial coherence of neighboring pixels, the method successfully tackles the unmixing problem.
Article
Environmental Sciences
Risheng Huang, Xiaorun Li, Yiming Fang, Zeyu Cao, Chaoqun Xia
Summary: This study proposes a robust hyperspectral unmixing method with practical learning-based hyperspectral image denoising. By formulating a close-to-reality noise model, calibrating noise parameters, and using a trained denoising network, the proposed method effectively handles HSI data and improves the unmixing performance.
Article
Environmental Sciences
Yannick Deville, Salah-Eddine Brezini, Fatima Zohra Benhalouche, Moussa Sofiane Karoui, Mireille Guillaume, Xavier Lenot, Bruno Lafrance, Malik Chami, Sylvain Jay, Audrey Minghelli, Xavier Briottet, Veronique Serfaty
Summary: In this paper, we introduce a specific hyperspectral mixing model for the sea bottom and an associated unmixing method that requires prior estimation of various parameters. We then analyze the model and show that it belongs to the general class of mixing models involving spectral variability. We use the IP-NMF unsupervised unmixing method to handle the spectral variability and demonstrate its effectiveness compared to a classical method. Test results with synthetic data further validate the proposed approach.
Article
Environmental Sciences
Shuhan Jia, Quanhua Zhao, Yu Li
Summary: This paper presents a new partitioned RP algorithm for reliable and efficient classification of large-size hyperspectral images. By dividing the HSI into multiple sub-HSIs and selecting a projection matrix that maximizes class separability, the proposed algorithm achieves reliable classification results in a short time.
Article
Computer Science, Hardware & Architecture
Xianghe Ma
Summary: Digital image processing plays a significant role in various fields, particularly in remote sensing image processing, involving the acquisition, enhancement, analysis, encoding, transmission, and storage of remote sensing images. However, the large volume of images produced by ultra-high resolution optical remote sensing satellites poses challenges for existing transmission, storage, and processing technologies. This paper proposes a spatio-temporal compression pipeline for remote sensing images, using lossy compression methods with ultra-high compression ratios to reduce overhead and maintain the image quality. Experimental results demonstrate that the proposed method outperforms classical image compression techniques like JPEG-2000.
Article
Geochemistry & Geophysics
Rob Heylen, Paul Scheunders
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2016)
Article
Geochemistry & Geophysics
Rob Heylen, Alina Zare, Paul Gader, Paul Scheunders
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2016)
Article
Geochemistry & Geophysics
Azam Karami, Rob Heylen, Paul Scheunders
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2016)
Article
Geochemistry & Geophysics
Rob Heylen, Mario Parente, Paul Scheunders
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2017)
Article
Environmental Sciences
Elham Kordi Ghasrodashti, Azam Karami, Rob Heylen, Paul Scheunders
Article
Geochemistry & Geophysics
Rob Heylen, Vera Andrejchenko, Zohreh Zahiri, Mario Parente, Paul Scheunders
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2019)
Article
Chemistry, Analytical
Brian G. Booth, Rob Heylen, Mohsen Nourazar, Dries Verhees, Wilfried Philips, Abdellatif Bey-Temsamani
Summary: We propose a method to improve melt pool stability in laser powder bed fusion by introducing temporal features and pore density modeling. A neural network model is used to link video features with pore densities, reducing the need for online printer interventions to avoid porosity.
Proceedings Paper
Engineering, Electrical & Electronic
Bikram Koirala, Rob Heylen, Paul Scheunders
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Thorvald Dox, Rob Heylen, Paul Scheunders
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Vera Andrejchenko, Rob Heylen, Wenzhi Liao, Wilfried Philips, Paul Scheunders
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
(2018)
Proceedings Paper
Geosciences, Multidisciplinary
Rob Heylen, Mario Parente, Paul Scheunders
2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
(2017)
Proceedings Paper
Acoustics
Rob Heylen, Mario Parente, Paul Scheunders
LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2017)
(2017)
Proceedings Paper
Engineering, Electrical & Electronic
Zahra Hashemi Nezhad, Azam Karami, Rob Heylen, Paul Scheunders
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
(2016)
Proceedings Paper
Engineering, Electrical & Electronic
Rob Heylen, Paul Scheunders, Alina Zare, Paul Gader
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
(2016)
Proceedings Paper
Engineering, Electrical & Electronic
Vera Andrejchenko, Rob Heylen, Paul Scheunders, Wilfried Philips, Wenzhi Liao
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
(2016)