scholarly journals An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing

2021 ◽  
Vol 13 (19) ◽  
pp. 3941
Author(s):  
Chuanlong Ye ◽  
Shanwei Liu ◽  
Mingming Xu ◽  
Bo Du ◽  
Jianhua Wan ◽  
...  
Keyword(s):  
Extraction Method ◽  
Extraction Methods ◽  
Abundance Estimation ◽  
Endmember Extraction ◽  
Remote Sensing Images ◽  
Spectral Variability ◽  
Single Spectrum ◽  
Hyperspectral Unmixing ◽  
Hyperspectral Remote Sensing Images ◽  
Multiscale Sampling

With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained.

A CBIR System for Hyperspectral Remote Sensing Images Using Endmember Extraction

2017 ◽  
Vol 31 (04) ◽  
pp. 1752001 ◽  
Author(s):  
Jing Zhang ◽  
Qianlan Zhou ◽  
Li Zhuo ◽  
Wenhao Geng ◽  
Suyu Wang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Superior Performance ◽  
Endmember Extraction ◽  
Remote Sensing Images ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Hyperspectral Remote Sensing Images ◽  
Hyperspectral Remote Sensing Image

With the rapid development of remote sensing technology, searching the similar image is a challenge for hyperspectral remote sensing image processing. Meanwhile, the dramatic growth in the amount of hyperspectral remote sensing data has stimulated considerable research on content-based image retrieval (CBIR) in the field of remote sensing technology. Although many CBIR systems have been developed, few studies focused on the hyperspectral remote sensing images. A CBIR system for hyperspectral remote sensing image using endmember extraction is proposed in this paper. The main contributions of our method are that: (1) the endmembers as the spectral features are extracted from hyperspectral remote sensing image by improved automatic pixel purity index (APPI) algorithm; (2) the spectral information divergence and spectral angle match (SID–SAM) mixed measure method is utilized as a similarity measurement between hyperspectral remote sensing images. At last, the images are ranked with descending and the top-[Formula: see text] retrieved images are returned. The experimental results on NASA datasets show that our system can yield a superior performance.

scholarly journals ASSESSING AND COMPARING THE PERFORMANCE OF ENDMEMBER EXTRACTION METHODS IN MULTIPLE CHANGE DETECTION USING HYPERSPECTRAL DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 571-575
Author(s):  
H. Jafarzadeh ◽  
M. Hasanlou
Keyword(s):  
Change Detection ◽  
Extraction Methods ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Hyperspectral Images ◽  
Endmember Extraction ◽  
Variable Splitting ◽  
Vertex Component Analysis ◽  
Hyperspectral Remote Sensing Images ◽  
Source Signals

Abstract. Endmember extraction is a process to identify the hidden pure source signals from the mixture. Endmember finding has become increasingly important in hyperspectral data exploitation because endmembers can be used to specify unknown particular spectral classes. This paper evaluates the change detection problem in bi-temporal hyperspectral remote sensing images using the unmixing process. A complete spectral unmixing process contains estimating the number of endmembers, endmember extraction and abundance estimation. Endmember extraction is a vital step in spectral unmixing of hyperspectral images. Hyperspectral change detection by unmixing has the potential to provide subpixel information from hyperspectral images. In this study, four methods including Simplex Identification via variable Splitting and Augmented Lagrangian (SISAL), N-finder algorithm (N-FINDR), Vertex Component Analysis (VCA), and Fast algorithm for linearly Unmixing (FUN) are used to produce multiple change detection maps. This paper explores and compares the performance of these methods in multiple change detection. The empirical results reveal the superiority of the FUN method in providing multiple change map with an overall accuracy of 87% and a kappa coefficient of 0.70.

scholarly journals Evaluation of DNA Extraction Methods Developed for Forensic and Ancient DNA Applications Using Bone Samples of Different Age

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 146
Author(s):  
Catarina Xavier ◽  
Mayra Eduardoff ◽  
Barbara Bertoglio ◽  
Christina Amory ◽  
Cordula Berger ◽  
...  
Keyword(s):  
Dna Extraction ◽  
Ancient Dna ◽  
Extraction Method ◽  
Fragment Size ◽  
Molecular Genetic ◽  
Extraction Methods ◽  
Degraded Dna ◽  
Size Analysis ◽  
Dna Fragments ◽  
Dna Amount

The efficient extraction of DNA from challenging samples, such as bones, is critical for the success of downstream genotyping analysis in molecular genetic disciplines. Even though the ancient DNA community has developed several protocols targeting small DNA fragments that are typically present in decomposed or old specimens, only recently forensic geneticists have started to adopt those protocols. Here, we compare an ancient DNA extraction protocol (Dabney) with a bone extraction method (Loreille) typically used in forensics. Real-time quantitative PCR and forensically representative typing methods including fragment size analysis and sequencing were used to assess protocol performance. We used four bone samples of different age in replicates to study the effects of both extraction methods. Our results confirm Loreille’s overall increased gain of DNA when enough tissue is available and Dabney’s improved efficiency for retrieving shorter DNA fragments that is beneficial when highly degraded DNA is present. The results suggest that the choice of extraction method needs to be based on available sample, degradation state, and targeted genotyping method. We modified the Dabney protocol by pooling parallel lysates prior to purification to study gain and performance in single tube typing assays and found that up to six parallel lysates lead to an almost linear gain of extracted DNA. These data are promising for further forensic investigations as the adapted Dabney protocol combines increased sensitivity for degraded DNA with necessary total DNA amount for forensic applications.

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