Spatial-Spectral Hyperspectral Endmember Extraction Using a Spatial Energy Prior Constrained Maximum Simplex Volume Approach

Shen, Xiangfei; Bao, Wenxing; Qu, Kewen

doi:10.1109/jstars.2020.2981402

Cited by 19 publications

(13 citation statements)

References 60 publications

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“…In [34], Xu et al created outliers by considering the idea that the spectral reflectance of several consecutive bands is significantly higher than that of other bands. In [3] and [25], the authors created outliers based on LMM, defined as…”

Section: Implementation Issuesmentioning

confidence: 99%

“…These include automatic morphological endmember extraction (AMEE) [22], spatial-spectral-based This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ endmember extraction (SSEE) [23], spatially weighted simplex strategy (SWSS) [24], and spatial energy-constrained maximum simplex volume (SENMAV) [25].…”

Section: Introductionmentioning

confidence: 99%

“…More importantly, some of them neglect the impact of outliers prior to endmember extraction. Normally, outliers are special patterns in data without a well-defined notion of normal behavior, and their signatures are spectrally distinct from their surroundings or background representation [25], [34], [35]. In this regard, we regard the generation process of outliers as two types: Geographically anomalous type and functionally anomalous type.…”

Section: Introductionmentioning

confidence: 99%

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Subspace-Based Preprocessing Module for Fast Hyperspectral Endmember Selection

Shen

Bao

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Endmember extraction algorithms (EEAs) play a crucial role in hyperspectral image (HSI) perception, and yet they normally suffer from three flaws: 1) High computational burden, 2) weak noise robustness, and 3) high outlier sensitivity. To solve these problems, this article proposes a fast subspace-based preprocessing module, called fast subspace-based preprocessing module (FSPM), to select a high-quality data subset for subsequent endmember extraction. Specifically, FSPM first transforms an HSI into a lowdimensional data subspace using singular value decomposition. For each component pair, FSPM then detects its convex hull vertices and proposes a local outlier score measurement to remove potential outliers. FSPM finally transforms determined data points into noise-reduced data space for the sake of identifying endmembers. The proposed FSPM sheds new light on the current preprocessing field, which can fast reduce noises and outliers as well as remove redundant data points. Based on various validation metrics, experiments conducted on both synthetic and real HSIs indicate that the proposed FSPM is superior to current state-of-the-art preprocessing techniques.

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Section: Implementation Issuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Subspace-Based Preprocessing Module for Fast Hyperspectral Endmember Selection

Shen

Bao

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…ATGP and SGA are similar in that they both extract endmembers with the maximum energy or the maximum distance. However, if there is burst noise in HSI, both algorithms will be seriously affected, leading to low extraction accuracy [35,36].…”

Section: Introductionmentioning

confidence: 99%

Interference-Suppressed and Cluster-Optimized Hyperspectral Target Extraction Based on Density Peak Clustering

Shang

Yang

Han

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Target extraction can provide a prior knowledge for spectral unmixing, unsupervised hyperspectral image classification and unsupervised target detection tasks, which is of great practice. Considering that the traditional endmember extraction algorithms such as automatic target generation process (ATGP) and simplex growing algorithm (SGA) are sensitive to the burst noise caused by hyperspectral sensor imaging, and cannot extract the appropriate target set to simultaneously guide the various tasks, a weighted target extraction algorithm based on the density peak clustering (DPC) is proposed in this paper. Firstly, DPC is extended to hyperspectral images, and the decision graph is generated according to the density and distance of each pixel to effectively classify the regions with different attributes like burst noise, cluster center, cluster boundary and cluster core. On the basis, three DPC-based weighted vectors are designed, and the weighted target extraction algorithms W-ATGP and W-SGA are put forward which allocates a lower weight than the core pixel to the interference so as to reduce its impact on ATGP and SGA while obtain the high-quality targets for various tasks. The experimental results on three datasets show that the accuracies of the target sets extracted by W-ATGP and W-SGA are higher than that of ATGP and SGA, no matter as the prior knowledge of spectral unmixing, unsupervised hyperspectral image classification or unsupervised target detection, which verifies the effectiveness of the proposed methods.Index Terms-hyperspectral image, density peak clustering, weighted target extraction, spectral unmixing, unsupervised hyperspectral image classification, unsupervised target detection.

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“…In LMM, the partial least squares regression and the multiple linear regressions are often used [60] [61] [62]. The NLMM assumes that the incident light follows multiple scattering phenomena [63] [70]. This paper focuses on the spectral unmixing method with LMM.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Approach for Estimation of Fractional Abundance of Nitrogen in Soil From Hyperspectral Data

Patel

Ghosh

Pande

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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One of the vital growth nutrient parameters of crops is soil nitrogen content. The ability to accurately grasp soil nutrient information is a prerequisite for scientific fertilization within the field of precision agriculture. Information pertaining to soil macronutrients, such as Nitrogen, may be obtained quickly through hyperspectral imaging techniques. Objective of this research is to explore the use of a deep learning network to estimate the abundance of urea fertilizer mixed soils for spectroradiometer data. The proposed approach was tested for silt clay and loamy types of soils. Spectral regions of 1899.2 nm for urea and 2195.1 nm for soils were identified as optimum spectral absorption features. The accuracy evaluation was performed using a linear regression model between actual and estimated abundances. At 1899.2 nm, the coefficient of determination (R 2) for mixed samples of urea and silt clay soil was found to be 0.945, whilst R 2 for urea mixed loamy soil were 0.954. Similarly, at 2195.1 nm, R 2 obtained 0.953 for urea mixed silt clay soil, and 0.944 for urea mixed loamy soil. The results show that the estimated abundances obtained through the derivative analysis for spectral unmixing (DASU)-based deep learning network, facilitated a greater accuracy in comparison to the sole use of DASU. These results were then verified through conventional chemical analysis methods. The outcome of this study determines the abundance of urea mixed soils. Therefore, it is inferred that the hyperspectral imaging technique may be utilized in-situ to assess the agricultural land's soil fertility status.

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Spatial-Spectral Hyperspectral Endmember Extraction Using a Spatial Energy Prior Constrained Maximum Simplex Volume Approach

Cited by 19 publications

References 60 publications

Subspace-Based Preprocessing Module for Fast Hyperspectral Endmember Selection

Subspace-Based Preprocessing Module for Fast Hyperspectral Endmember Selection

Interference-Suppressed and Cluster-Optimized Hyperspectral Target Extraction Based on Density Peak Clustering

Deep-Learning-Based Approach for Estimation of Fractional Abundance of Nitrogen in Soil From Hyperspectral Data

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