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.
Article
Environmental Sciences
Ke Wu, Tao Chen, Ying Xu, Dongwei Song, Haishan Li
Summary: The study proposes a novel change detection approach which tackles the challenges of mixed pixels and variations in endmembers by utilizing image stacking and dividing based on spectral unmixing, resulting in improved detection accuracy. Experimental results show that in simulated data, the overall accuracy and Kappa coefficient values reached 99.61% and 0.99. For two sets of real data, the highest accuracy rates were 93.26% and 80.85%, showing an increase of 14.88% and 13.42% compared to the worst results. Additionally, the Kappa coefficient values were consistent with the accuracy rates.
Article
Environmental Sciences
Ying Cheng, Liaoying Zhao, Shuhan Chen, Xiaorun Li
Summary: Spectral unmixing is an important topic in hyperspectral image analysis as it deals with the presence of multiple sources in images and the variability in spectral signatures caused by environmental conditions. Various spectral mixing models have been proposed, but their interpretation is often insufficient and the corresponding unmixing algorithms are classical techniques. This paper introduces a spectral unmixing network based on a scaled and perturbed linear mixing model, incorporating deep learning techniques for determining abundances, scales, and perturbations. The proposed approach outperforms other competitors in both synthetic and real data sets.
Article
Environmental Sciences
Zhao Wang, Jianzhao Li, Yiting Liu, Fei Xie, Peng Li
Summary: This paper proposes an adaptive surrogate-assisted endmember extraction (ASAEE) framework based on intelligent optimization algorithms to solve the problem of endmember extraction in hyperspectral remote sensing images. By establishing a surrogate-assisted model to reduce the time cost of intelligent algorithms and using an adaptive weight surrogate-assisted model selection strategy, the accuracy and running time of endmember extraction are improved.
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
Chemistry, Analytical
Salah Eddine Brezini, Yannick Deville
Summary: The aim of fusing hyperspectral and multispectral images is to improve their spatial resolutions. However, most fusion methods do not consider spectral variability, leading to reduced quality of the sharpened products. This paper introduces a new method called HSB-SV, which addresses spectral variability using spectra bundles-based method and sparsity-based method.
Article
Environmental Sciences
Jing Liu, Yang Li, Feng Zhao, Yi Liu
Summary: In this study, a spectral fractional-differentiation (SFD) feature is proposed to extract effective features for the terrain classification of hyperspectral remote-sensing images (HRSIs). A criterion for selecting the fractional-differentiation order based on maximizing data separability is also introduced. The effectiveness of the SFD feature is verified using four traditional classifiers and five network models, and the results show that the SFD feature can effectively improve the accuracy of terrain classification for HRSIs, especially in cases with small-size training samples.
Article
Engineering, Electrical & Electronic
Ricardo Augusto Borsoi, Tales Imbiriba, Jose Carlos Moreira Bermudez, Cedric Richard
Summary: The article proposes an efficient multitemporal SU method that utilizes the high temporal correlation between the abundances to provide more accurate results at a lower computational complexity. By separately addressing the endmember selection and the abundance estimation problems, a simpler solution without sacrificing accuracy is achieved. A strategy to detect and address abrupt abundance variations in time is also proposed.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2021)
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
Chemistry, Multidisciplinary
Baohua Jin, Yunfei Zhu, Wei Huang, Qiqiang Chen, Sijia Li
Summary: The purpose of hyperspectral unmixing is to obtain the spectral features and proportions of materials in a hyperspectral image. However, spectral variabilities make it difficult to accurately extract these features. To address this issue, this study proposes an efficient attention-based convolutional neural network and a convolution block attention module. Experimental results demonstrate that this method outperforms other unmixing methods.
APPLIED SCIENCES-BASEL
(2022)
Article
Geochemistry & Geophysics
Chunyang Cui, Yanfei Zhong, Xinyu Wang, Liangpei Zhang
Summary: This article introduces a dataset called RMMS, which is used to quantitatively evaluate the unmixing accuracy of different algorithms. The dataset includes a simple mixture scene and a complex mixture scene, taking into consideration point, line, and polygon characteristics, and increasing the challenge of spectral unmixing with spectral similarity. By using the RMMS dataset, registration errors between RGB and hyperspectral data can be avoided, and the purity of the endmembers can be ensured.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Xinyu Wang, Yanfei Zhong, Chunyang Cui, Liangpei Zhang, Yanyan Xu
Summary: The paper introduces a saliency-based autonomous endmember detection algorithm to jointly estimate the virtual dimensionality. By utilizing abundance anomaly values and superpixel priors, it successfully distinguishes endmembers from noise and automatically determines the virtual dimensionality.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Lyuyang Tong, Bo Du, Rong Liu, Liangpei Zhang, Kay Chen Tan
Summary: Endmember extraction is crucial in hyperspectral unmixing, involving a multiobjective optimization problem of maximizing volume and minimizing root-mean-square error. Balancing these objectives is challenging, but a (mu + lambda) multiobjective differential evolution algorithm has been developed to address this challenge, showing superiority over other methods in experimental results.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Daniele Cerra, Miguel Pato, Kevin Alonso, Claas Koeller, Mathias Schneider, Raquel de los Reyes, Emiliano Carmona, Rudolf Richter, Franz Kurz, Peter Reinartz, Rupert Mueller
Summary: This paper introduces the DLR HySU benchmark dataset, the first dataset to provide real imaging spectrometer data for hyperspectral unmixing experiments, including airborne hyperspectral and RGB imagery, as well as ground-based reflectance measurements.
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)
