Hyperspectral Image Classification using Spectral Angle Mapper

Chakravarty, Sujata; Paikaray, Bijay; Mishra, Rutuparnna; Dash, Satyabrata

doi:10.1109/wiecon-ece54711.2021.9829585

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…Six benchmark datasets were used to conduct rigorous studies. [18][19][20] The experimental results depict the superiority of the suggested deep learning-based models when compared with other state-ofthe-art methods. In Ref.…”

Section: Related Workmentioning

confidence: 89%

“…For HSI classification, it recommended four novel deep learning models: two-dimensional (2D)-CNN, three-dimensional (3D)-CNN, recurrent 2D-CNN (R-2D-CNN), and recurrent 3D-CNN (R-3D-CNN). Six benchmark datasets were used to conduct rigorous studies 18 – 20 The experimental results depict the superiority of the suggested deep learning-based models when compared with other state-of-the-art methods.…”

Section: Related Workmentioning

confidence: 99%

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Feature extraction and classification of hyperspectral imaging using minimum noise fraction and deep convolutional neural network

et al. 2022

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Remote sensing technology is constantly developing, which has greatly expanded the use of hyperspectral imaging (HSI). In the arena of remote sensing, the classification of HSI has become a challenging topic. The unique properties of hyperspectral data make accurate categorization difficult. In recent years, deep structured learning has emerged as an effective feature extraction technique for effectively addressing nonlinear problems. It is now extensively used to solve a variety of image processing problems. Deep learning is used to classify images and has shown good performance in recent years. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on HSI categorization utilizing several machine learning approaches, such as support vector machine, K-nearest neighbor, and convolutional neural network (CNN). To reduce the number of superfluous and noisy bands in the dataset, principal component analysis and minimum noise fraction (MNF) were utilized. Different performance evaluation measures, such as time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and G-mean, were taken to prove the efficacy of the models. The simulation results show that the combination of MNF and CNN produces better classification accuracy compared with the other considered models.

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Section: Related Workmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Feature extraction and classification of hyperspectral imaging using minimum noise fraction and deep convolutional neural network

et al. 2022

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“…The Spectral Angle Mapper (SAM) metric ( 2) allows to define a segmentation criterion. The SAM algorithm takes as inputs a reference spectral signature and a test spectral signature (TS) as q -dimensional vectors and outputs the angle between them [30]. A low SAM value indicates similarity between the reference and test spectral signatures.…”

Section: IImentioning

confidence: 99%

Analysing histology hyperspectral images: Does tissue thickness matter?

Santana-Nunez,

Quintana-Quintana,

Fabelo

et al. 2024

Biomedical Spectroscopy, Microscopy, and Imaging III

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Cancer is one of the leading causes of death, thereby, contributing to their quick diagnosis or treatment is of greatest importance. Nowadays, tumours are mainly diagnosed and graded histologically using biopsies. Since the images need to be sharp to distinguish biological structures, samples are thinly sliced (3-5 μm) to avoid scattering and contrast is obtained using highly absorbance dyes (e.g., Haematoxylin and Eosin (H&E)). RGB (Red-Green-Blue) cameras have been widely employed to acquire those images, while new approaches, such as Hyperspectral (HS) Imaging (HSI), have been arising to obtain a greater amount of spectral information from the samples. However, in order to have diffuse light for the HS cameras to capture it, the thickness of the sample should be bigger than the ones employed in conventional microscopy. This work aims to characterize the influence of tissue thickness of histology breast samples sectioned at 2 and 3 μm on their spectral signatures. Based on the H&E transmittance spectra peaks, HS images were segmented into three structures: stroma (eosin-stained), nuclei (haematoxylin-stained), and background (non-stained). Results show that, spatially, in 3 μm samples there are more cells imaged than in 2 μm samples. Moreover, spectrally, 3 μm samples proportionate higher spectral contrast than 2 μm samples due the greater interaction of light with tissue, denoting them as more suitable for microscopic HSI.

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