Comparison of spectral angle mapper and support vector machine classification methods for mapping skin burn using hyperspectral imaging

Parasca, Sorin Viorel; Manea, Dragoș

doi:10.1117/12.2319267

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…In this regard, the classical machine learning algorithms tend to score low in their analysis [9], the main problem remaining to be the curse of dimensionality, related to the number of channels in an HS image. A common approach is to employ a linear model for HS image analysis such as SAM [10] or Partial Least Squares (PLS) [11] to determine the similarity of an unknown spectral signature with a known one. In such approaches, the high dimensionality of the HS data is normally pre-processed to a lower dimension using dimensionality reduction techniques such as PCA [12], ICA [13], (multi) band selection [14], etc.…”

Section: Related Workmentioning

confidence: 99%

On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging

Dijkstra¹,

Aghaei²,

Jaarsma³

et al. 2022

Preprint

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The importance of plastic waste recycling is undeniable. In this respect, computer vision and deep learning enable solutions through the automated analysis of short-wave-infrared hyper-spectral images of plastics. In this paper, we offer an exhaustive empirical study to show the importance of efficient model selection for resolving the task of hyper-spectral image segmentation of various plastic flakes using deep learning. We assess the complexity level of generic and specialized models and infer their performance capacity: generic models are often unnecessarily complex. We introduce two variants of a specialized hyper-spectral architecture, PlasticNet, that outperforms several well-known segmentation architectures in both performance as well as computational complexity. In addition, we shed lights on the significance of signal pre-processing within the realm of hyper-spectral imaging. To complete our contribution, we introduce the largest, most versatile hyper-spectral dataset of plastic flakes of four primary polymer types.

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Section: Related Workmentioning

confidence: 99%

On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging

Dijkstra¹,

Aghaei²,

Jaarsma³

et al. 2022

Preprint

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“…SAM is a supervised classification technique for HSIC [29]. SAM classifier admits very quick classification using the spectral angle information of HSI data.…”

Section: Spectral Angle Mapper (Sam)mentioning

confidence: 99%

Hyperspectral Image Classification

Gogineni¹,

Chaturvedi²

2020

Processing and Analysis of Hyperspectral Data

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Hyperspectral image (HSI) classification is a phenomenal mechanism to analyze diversified land cover in remotely sensed hyperspectral images. In the field of remote sensing, HSI classification has been an established research topic, and herein, the inherent primary challenges are (i) curse of dimensionality and (ii) insufficient samples pool during training. Given a set of observations with known class labels, the basic goal of hyperspectral image classification is to assign a class label to each pixel. This chapter discusses the recent progress in the classification of HS images in the aspects of Kernel-based methods, supervised and unsupervised classifiers, classification based on sparse representation, and spectral-spatial classification. Further, the classification methods based on machine learning and the future directions are discussed.

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“…In a recent prospective study performed by Schulz et al [10], a burn index was calculated, but the system is not yet able to generate maps. A step forward was taken by Calin et al [11] who compared support vector machine and spectral angle mapper as classifiers for burn hyperspectral images taking the method at the threshold of machine learning and demonstrating that the former gave better results. A close related imaging method (spatial frequency domain imaging) was combined with machine learning method (support vector machine) in [12] and a 92.5% accuracy in predicting burn severity was reported.…”

Section: Introductionmentioning

confidence: 99%

Burn characterization using object‐oriented hyperspectral image classification

Parasca

2022

Journal of Biophotonics

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This paper presents a new approach based on hyperspectral imaging combined with an object‐oriented classification method that allows the generation of burn depth classification maps facilitating easier characterization of burns. Hyperspectral images of 14 patients diagnosed with burns on the upper and lower limbs were acquired using a pushbroom hyperspectral imaging system. The images were analyzed using an object‐oriented classification approach that uses objects with specific spectral, textural and spatial attributes as the minimum unit for classifying information. The method performance was evaluated in terms of overall accuracy, sensitivity, precision and specificity computed from the confusion matrix. The results revealed that the approach proposed in this study performed well in differentiating burn classes with a high level of overall accuracy (95.99% ± 0.60%), precision (97.30% ± 2.46%), sensitivity (97.23% ± 3.02%) and specificity (98.02% ± 1.98%). In conclusion, the object‐based approach for burns hyperspectral images classification can provide maps that can help surgeons identify with better precision different depths of burn wounds.

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Comparison of spectral angle mapper and support vector machine classification methods for mapping skin burn using hyperspectral imaging

Cited by 7 publications

References 34 publications

On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging

On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging

Hyperspectral Image Classification

Burn characterization using object‐oriented hyperspectral image classification

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