Hyperspectral Endmember Extraction Techniques

Kale, K. V.; Solankar, Mahesh M.; Nalawade, Dhananjay B.

doi:10.5772/intechopen.88910

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…The poorer resolution caused problems may to an extent be mitigated by employing hyperspectral endmember extraction methods as outlined in [10]. These methods are designed to decompose spectra of individual pixels into different materials (endmembers) in the image, this helps in resolving the fringe regions where in a single pixels spectra of multiple materials are projected (due to insufficient resolution, blur or other factors).…”

Section: A Results and Discussionmentioning

confidence: 99%

On Hyperspectral Analysis of Water Soluble Writing Inks

Zemčík¹,

Horák²

2023

Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected Papers.

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In this short paper a possibility of using Hyperspectral Imaging based analysis of water soluble inks is investigated. The current research has investigated this method mostly for marker inks, ballpoint pen inks and gel inks, yet curiously the whole water soluble ink class seems underrepresented. Samples were prepared and hyperspectral images of said samples taken, and some preliminary statistical analysis was performed. The data at this stage suggests that at least some samples are visually distinguishable from other samples, while certain other samples are virtually indistinguishable. A method for applying this knowledge in a handwritten text ink identification is briefly outlined and implemented in a simplified way, the results are promising on good input data, but poor on subpar images. The weaknesses of the method are discussed and potential improvement strategies proposed.

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Section: A Results and Discussionmentioning

confidence: 99%

On Hyperspectral Analysis of Water Soluble Writing Inks

Zemčík¹,

Horák²

2023

Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected Papers.

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“…Geometrical algorithms such as N-findr and Pixel Purity Index (PPI) are the most common types of supervised unmixing methods [59]. Both of these methods requires the assumption that each endmember is represented in at least one pure pixel within the image.…”

Section: Hyperspectral Unmixingmentioning

confidence: 99%

Brain Tumor Classification Using Hyperspectral Image Analysis

Baig

2024

Preprint

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<p>The capability of Hyperspectral Imaging (HSI) in rapidly acquiring abundant reflectance data in a non-invasive manner, makes it an ideal tool for obtaining diagnostic information about tissue pathology. Identifying features that provide discriminatory clues for specific pathologies will assist in understanding their underlying biochemical characteristics. In this thesis, brain cancer HSI data was analyzed to arrive at two approaches for brain tumor classification: (1) identification of simple morphological features from the pixel spectra (2) use of a computationally efficient data decomposition method to automatically identify discriminatory bands. Using a database of 26 brain cancer HS images and simple morphological features, maximum pixel classification accuracies of 87.9% for binary (tumor, normal) and 78.6% for multigroup (tumor, normal, hypervascular, and background) classification were achieved. The proposed band selection approach achieved maximum pixel classification accuracies of 99.6% for binary and 94.7% for multigroup classification with 3-times reduction in feature-set compared to the benchmark.</p>

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“…In addition to deep learning, other algorithmic models based on machine learning are applied in hyperspectral data processing. Examples are: PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) used for extracting features of interest, similarities, and dissimilarities in the data (Dua et al, 2020;Hsieh and Kiang, 2020), and SVM (Support Vector Machine), for solving linear classification problems, which are widely employed for HSI data classification due to their ability to effectively separate heterogeneous samples on the mapped plane (Kale et al, 2017). Although SVM was designed to decipher linear datasets, its usage can be extended to non-linear data when combined with kernel methods (Melgani and Bruzzone, 2004).…”

Section: Hyperspectral Imaging and Artificial Intelligencementioning

confidence: 99%

Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges

Ferreira,

Carvalho,

Jorge

et al. 2024

Front. Plant Sci.

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Food security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. With the expansion of the international seed market with global movement of this propagative plant material, and considering that about 90% of economically important crops grown from seeds, seed pathology emerged as an important discipline. Seed health testing is presently part of quality analysis and carried out by seed enterprises and governmental institutions looking forward to exclude a new pathogen in a country or site. The development of seedborne pathogens detection methods has been following the plant pathogen detection and diagnosis advances, from the use of cultivation on semi-selective media, to antibodies and DNA-based techniques. Hyperspectral imaging (HSI) associated with artificial intelligence can be considered the new frontier for seedborne pathogen detection with high accuracy in discriminating infected from healthy seeds. The development of the process consists of standardization of methods and protocols with the validation of spectral signatures for presence and incidence of contamined seeds. Concurrently, epidemiological studies correlating this information with disease outbreaks would help in determining the acceptable thresholds of seed contamination. Despite the high costs of equipment and the necessity for interdisciplinary collaboration, it is anticipated that health seed certifying programs and seed suppliers will benefit from the adoption of HSI techniques in the near future.

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Hyperspectral Endmember Extraction Techniques

Cited by 9 publications

References 30 publications

On Hyperspectral Analysis of Water Soluble Writing Inks

On Hyperspectral Analysis of Water Soluble Writing Inks

Brain Tumor Classification Using Hyperspectral Image Analysis

Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges

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