Spectral angle mapping, spectral information divergence, and principal component analysis of the ASTER SWIR data for exploration of porphyry copper mineralization in the Sarduiyeh area, Kerman province, Iran

2014 ◽  
Vol 6 (1) ◽  
pp. 49-58 ◽  
Author(s):  
Mohammad Khaleghi ◽  
Hojjatollah Ranjbar ◽  
Jamshid Shahabpour ◽  
Mehdi Honarmand
Keyword(s):  
Principal Component Analysis ◽  
Porphyry Copper ◽  
Principal Component ◽  
Component Analysis ◽  
Spectral Information ◽  
Copper Mineralization ◽  
Information Divergence ◽  
Porphyry Copper Mineralization ◽  
Spectral Angle

scholarly journals Data Processing Method Applying Principal Component Analysis and Spectral Angle Mapper for Imaging Spectroscopic Sensors

2008 ◽  
Vol 8 (7) ◽  
pp. 1310-1316 ◽  
Author(s):  
P. Beatriz Garcia-Allende ◽  
Olga M. Conde ◽  
JesÚs Mirapeix ◽  
Ana M. Cubillas ◽  
JosÉ M. Lopez-Higuera
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Principal Component ◽  
Component Analysis ◽  
Processing Method ◽  
Spectral Angle Mapper ◽  
Data Processing Method ◽  
Spectroscopic Sensors ◽  
Spectral Angle

scholarly journals Hyperspectral image classification based on spectral-spatial kernel principal component analysis network

2020 ◽  
Vol 165 ◽  
pp. 03001
Author(s):  
Yanguo Fan ◽  
Shizhe Hou ◽  
Dingfeng Yu
Keyword(s):  
Principal Component Analysis ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Spectral Information ◽  
Original Structure ◽  
Hyperspectral Image Classification

Hyperspectral imagery contains both spectral information and spatial relationships among pixels. How to combine spatial information with spectral information effectively has always been a research hotspot of hyperspectral image classification. In this paper, a Spatial-Spectral Kernel Principal Component Analysis Network (SS-KPCANet) was proposed. The network is developed from the original structure of Principal Component Analysis Network. In which PCA is replaced by KPCA to extract more nonlinear features. In addition, the combination of spatial and spectral features also improves the performance of the network. At the end of the network, neighbourhood correction is added to further improve the classification accuracy. Experiments on three datasets show the effectiveness of the proposed method. Comparison with state-of-the-art deep learning-based methods indicate that the proposed method needs less training samples and has better performance.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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