Application of nonnegative principal component analysis in hyperspectral imaging

2006 ◽  
Author(s):  
Peter Bajorski
Keyword(s):  
Principal Component Analysis ◽  
Hyperspectral Imaging ◽  
Principal Component ◽  
Component Analysis

scholarly journals Investigation of the Performance of Hyperspectral Imaging by Principal Component Analysis in the Prediction of Healing of Diabetic Foot Ulcers

2018 ◽  
Vol 4 (12) ◽  
pp. 144 ◽  
Author(s):  
Qian Yang ◽  
Shen Sun ◽  
William Jeffcoate ◽  
Daniel Clark ◽  
Alison Musgove ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Oxygen Saturation ◽  
Hyperspectral Imaging ◽  
Ulcer Healing ◽  
Diabetic Foot ◽  
Principal Component ◽  
Component Analysis ◽  
Foot Ulcers ◽  
Diabetic Foot Ulcers ◽  
Care Providers

Diabetic foot ulcers are a major complication of diabetes and present a considerable burden for both patients and health care providers. As healing often takes many months, a method of determining which ulcers would be most likely to heal would be of great value in identifying patients who require further intervention at an early stage. Hyperspectral imaging (HSI) is a tool that has the potential to meet this clinical need. Due to the different absorption spectra of oxy- and deoxyhemoglobin, in biomedical HSI the majority of research has utilized reflectance spectra to estimate oxygen saturation (SpO2) values from peripheral tissue. In an earlier study, HSI of 43 patients with diabetic foot ulcers at the time of presentation revealed that ulcer healing by 12 weeks could be predicted by the assessment of SpO2 calculated from these images. Principal component analysis (PCA) is an alternative approach to analyzing HSI data. Although frequently applied in other fields, mapping of SpO2 is more common in biomedical HSI. It is therefore valuable to compare the performance of PCA with SpO2 measurement in the prediction of wound healing. Data from the same study group have now been used to examine the relationship between ulcer healing by 12 weeks when the results of the original HSI are analyzed using PCA. At the optimum thresholds, the sensitivity of prediction of healing by 12 weeks using PCA (87.5%) was greater than that of SpO2 (50.0%), with both approaches showing equal specificity (88.2%). The positive predictive value of PCA and oxygen saturation analysis was 0.91 and 0.86, respectively, and a comparison by receiver operating characteristic curve analysis revealed an area under the curve of 0.88 for PCA compared with 0.66 using SpO2 analysis. It is concluded that HSI may be a better predictor of healing when analyzed by PCA than by SpO2.

Hyperspectral imaging combined with principal component analysis for bruise damage detection on white mushrooms (Agaricus bisporus)

2008 ◽  
Vol 22 (3-4) ◽  
pp. 259-267 ◽  
Author(s):  
A. A. Gowen ◽  
C. P. O'Donnell ◽  
M. Taghizadeh ◽  
P. J. Cullen ◽  
J. M. Frias ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Hyperspectral Imaging ◽  
Agaricus Bisporus ◽  
Principal Component ◽  
Component Analysis

scholarly journals Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth

2021 ◽  
Vol 12 ◽  
Author(s):  
Yiqi Huang ◽  
Jie Li ◽  
Rui Yang ◽  
Fukuan Wang ◽  
Yanzhou Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Dimension Reduction ◽  
Hyperspectral Imaging ◽  
Execution Time ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Economic Damage ◽  
Mikania Micrantha

Mile-a-minute weed (Mikania micrantha Kunth) is considered as one of top 100 most dangerous invasive species in the world. A fast and accurate detection technology will be needed to identify M. micrantha. It will help to mitigate the extensive ecologic and economic damage on our ecosystems caused by this alien plant. Hyperspectral technology fulfills the above requirement. However, when working with hyperspectral images, preprocessing, dimension reduction, and classifier are fundamental to achieving reliable recognition accuracy and efficiency. The spectral data of M. micrantha were collected using hyperspectral imaging in the spectral range of 450–998 nm. A different combination of preprocessing methods, principal component analysis (for dimension reduction), and three classifiers were used to analyze the collected hyperspectral images. The results showed that a combination of Savitzky-Golay (SG) smoothing, principal component analysis (PCA), and random forest (RF) achieved an accuracy (A) of 88.71%, an average accuracy (AA) of 88.68%, and a Kappa of 0.7740 with an execution time of 9.647 ms. In contrast, the combination of SG, PCA and a support vector machine (SVM) resulted in a weaker performance in terms of A (84.68%), AA(84.66%), and Kappa (0.6934), but with less execution time (1.318 ms). According to the requirements for specific identification accuracy and time cost, SG-PCA-RF and SG-PCA-SVM might represent two promising methods for recognizing M. micrantha in the wild.

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