Assessment of spectral reduction techniques for endmember extraction in unmixing of hyperspectral images

George, Elizabeth Baby; Ternikar, Chirag Rajendra; Ghosh, Ridhee; Kumar, D. Nagesh; Gomez, Cécile; Ahmad, Touseef; Sahadevan, Anand S; Gupta, Praveen Kumar; Misra, Arundhati

doi:10.1016/j.asr.2022.06.028

Cited by 3 publications

(2 citation statements)

References 71 publications

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“…SMA of any kind requires appropriate endmember selection to conduct the unmixing process. Frequently, researchers stand by more traditional endmember extraction methodologies that involve various methods of spectral reduction through techniques such as principal component analysis (PCA) or minimum noise fraction (MNF), followed by endmember extraction algorithms such as the pixel purity index (PPI) or the automated target generation process (ATGP) [7,16,30]. However, it is difficult to truly automate the proposed methodology using the aforementioned algorithms as they often require some level of user intervention to match the correct endmember class to the extracted endmember spectra.…”

Section: Endmembers and Spectral Librariesmentioning

confidence: 99%

Automated Surface Runoff Estimation with the Spectral Unmixing of Remotely Sensed Multispectral Imagery

Campo,

Tamagnone,

Schumann

2023

Remote Sensing

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This work presents a methodology for the hydrological characterization of natural and urban landscapes, focusing on accurate estimations of infiltration capacity and runoff characteristics. By combining existing methods from the literature, we created a systemic process that integrates satellite-based vegetation maps, topography, and soil permeability data. This process generates a detailed vegetation classification and slope-corrected composite curve number (CNcα) map using information at the subpixel level, which is crucial for estimating excess runoff during intense precipitation events. The algorithm designed with this methodology is automated and utilizes freely accessible multispectral imagery. Leveraging the vegetation–impervious–soil (V-I-S) model, it is assumed that land cover comprises V-I-S components at each pixel. Automated Music and spectral Separability-based Endmember Selection is employed on a generic spectral library to obtain the most relevant V-I-S endmember spectra for a particular image, which is then employed in multiple endmember spectral mixture analysis to obtain V-I-S fraction maps. The derived fractions are utilized in combination with the Normalized Difference Vegetation Index and the Modified Normalized Difference Water Index to adapt the CNcα map to different seasons and climatic conditions. The methodology was applied to Esch-sur-Alzette, Luxembourg, over a four-year period to validate the methodology and quantify the increase in the impervious surface area in the commune and the relationship with the runoff dynamics. This approach provides valuable insights into infiltration and runoff dynamics across diverse temporal and geographic ranges.

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Section: Endmembers and Spectral Librariesmentioning

confidence: 99%

Automated Surface Runoff Estimation with the Spectral Unmixing of Remotely Sensed Multispectral Imagery

Campo,

Tamagnone,

Schumann

2023

Remote Sensing

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“…With the help of advanced and state-of-the-art hyperspectral sensors, covering a wide spectral range, more data is available, and the complexity beyond the conventional human visual power or imaging technology is overcome. The impressive spectral information is able to be used for the detection of objects, materials, or conditions through anomalies; the diverse disciplines in which this information can be utilized are environmental monitoring, agriculture, mineralogy, and many other fields [9][10][11][12][13][14][15][16][17]. On the other hand, the richness of hyperspectral data as a disadvantage brings quite a few significant problems that are coming from difficulty to process effectively and deal with complication.…”

Section: Introductionmentioning

confidence: 99%

Charting the evolution: bibliometric perspectives on anomaly detection within hyperspectral domains

Obaideen,

Bonny,

AlShabi

2024

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

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In this article the results of the disruption module in the hyperspectral image field are analyzed and present itself. The aim is to see the major concerns, techniques and potential areas that can be researched as topics in the bibliometric method. Highly specified hyper-spectral spectroscopy that can simultaneously analyze the spectral data beyond the visual spectrum down even to the nanoscale. It can be used for various reasons, including environmental management, agriculture, and mineralogy. Unlike small data systems, where patterns are simple to identify, in large scale systems, where there is huge data quantity to study, there should be a wide variety of complexity of algorithms to use in reliable anomaly detection. This research bases its methodology on a two-layer mechanism which is simply to conduct very thorough literature review and deep bibliometric analysis to showcase the innovations in the field of anomaly detection of hyperspectral radar. The role of the states of estimation and the parameters, trained and objective, filtered, control rules, and fuzzy logic are some aspects, among the many, that is coming to the fore during the anomaly detection process. This paper aims to carry out a detailed analysis to mainly focus on the technical and methodological advancements that have reshaped the research area. It also shows that priority should be given to this aspect and that anime detection is the most challenging part of hyperspectral. What is more, this process gives numerous valuable signals about potential routes for future research. The results point to the dominant feature of the developed strategy and analysis which was mostly based on the special of multi-dimensional and transdisciplinary thinking.

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Flashover Characteristics of Composite Insulators with Different Levels of Guano Adhesion and Detection Based on Hyperspectral Technique

Meng,

Jia,

Yao

et al. 2024

Proceedings of the 2024 2nd International Conference on Internet of Things and Cloud Computing Technology

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Assessment of spectral reduction techniques for endmember extraction in unmixing of hyperspectral images

Cited by 3 publications

References 71 publications

Automated Surface Runoff Estimation with the Spectral Unmixing of Remotely Sensed Multispectral Imagery

Automated Surface Runoff Estimation with the Spectral Unmixing of Remotely Sensed Multispectral Imagery

Charting the evolution: bibliometric perspectives on anomaly detection within hyperspectral domains

Flashover Characteristics of Composite Insulators with Different Levels of Guano Adhesion and Detection Based on Hyperspectral Technique

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