scholarly journals Real-Time Noise Removal for Line-Scanning Hyperspectral Devices Using a Minimum Noise Fraction-Based Approach

Sensors ◽  
2015 ◽  
Vol 15 (2) ◽  
pp. 3362-3378 ◽  
Author(s):  
Asgeir Bjorgan ◽  
Lise Randeberg
Keyword(s):  
Real Time ◽  
Noise Removal ◽  
Minimum Noise Fraction ◽  
Line Scanning ◽  
Noise Fraction ◽  
Minimum Noise

Noise removal for airborne time domain electromagnetic data based on minimum noise fraction

2018 ◽  
Vol 49 (2) ◽  
pp. 127-133
Author(s):  
Yue Li ◽  
Yang Meng ◽  
Yiming Lu ◽  
Lingqun Wang ◽  
Bin Xie ◽  
...  
Keyword(s):  
Time Domain ◽  
Noise Removal ◽  
Electromagnetic Data ◽  
Minimum Noise Fraction ◽  
Time Domain Electromagnetic ◽  
Noise Fraction ◽  
Minimum Noise

scholarly journals Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images

2020 ◽  
Vol 37 (5) ◽  
pp. 812-822
Author(s):  
Behnam Asghari Beirami ◽  
Mehdi Mokhtarzade
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Minimum Noise Fraction ◽  
Vector Machines ◽  
Noise Covariance ◽  
Noise Fraction ◽  
Minimum Noise

In this paper, a novel feature extraction technique called SuperMNF is proposed, which is an extension of the minimum noise fraction (MNF) transformation. In SuperMNF, each superpixel has its own transformation matrix and MNF transformation is performed on each superpixel individually. The basic idea behind the SuperMNF is that each superpixel contains its specific signal and noise covariance matrices which are different from the adjacent superpixels. The extracted features, owning spatial-spectral content and provided in the lower dimension, are classified by maximum likelihood classifier and support vector machines. Experiments that are conducted on two real hyperspectral images, named Indian Pines and Pavia University, demonstrate the efficiency of SuperMNF since it yielded more promising results than some other feature extraction methods (MNF, PCA, SuperPCA, KPCA, and MMP).

High Resolution Image Classification Method Combining with Minimum Noise Fraction Rotation and Convolution Neural Network

2017 ◽  
Vol 54 (10) ◽  
pp. 102801
Author(s):  
陈 洋 Chen Yang ◽  
范荣双 Fan Rongshuang ◽  
王竞雪 Wang Jingxue ◽  
吴增林 Wu Zenglin ◽  
孙汝星 Sun Ruxing
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Image Classification ◽  
Convolution Neural Network ◽  
Classification Method ◽  
Resolution Image ◽  
High Resolution Image ◽  
Minimum Noise Fraction ◽  
Noise Fraction ◽  
Minimum Noise

Mapping lithology in Canada's Arctic: application of hyperspectral data using the minimum noise fraction transformation and matched filtering

2005 ◽  
Vol 42 (12) ◽  
pp. 2173-2193 ◽  
Author(s):  
J R Harris ◽  
D Rogge ◽  
R Hitchcock ◽  
O Ijewliw ◽  
D Wright
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Matched Filtering ◽  
Lithological Mapping ◽  
Minimum Noise Fraction ◽  
Logical Mapping ◽  
End Members ◽  
Noise Fraction ◽  
Minimum Noise

A test site in southern Baffin Island, Canada has been established to study the applications of hyperspectral data to lithological mapping. Good bedrock exposure and minimal vegetation cover provide an ideal environment for the evaluation of hyperspectral remote sensing. Airborne PROBE hyperspectral data were collected over the study site during the summer of 2000. Processing methods involved (1) applying a minimum noise fraction (MNF) transformation to the data and visual interpretation of a ternary colour MNF image to produce a lithological–compositional map, and (2) selection of end members from the MNF image followed by matched filtering based on the selected end members to produce a lithological–compositional map. Both methods have produced a lithological map that compares favourably with the existing geological map. Although lichen imparts a similarity to the spectra throughout the visible and near infrared and short-wave infrared ranges, this study has shown that enough variability in the spectra as a function of different mineralogy was present to successfully discriminate one major lithological group (metatonalites) and three compositional units (psammites, quartzites, and monzogranites). Vegetation could be clearly distinguished, which in this area only is a good proxy for mapping metagabbroic rocks. Furthermore, discrimination of slightly different compositional units within the psammites and the metatonalites was also possible. The results from this study indicate that hyperspectral remotely sensed imagery holds promise for lithological mapping in Canada's North, although further analysis is required in different geologic environments in Canada's North to validate hyperspectral remote sensing as a useful aid to litho logical mapping.

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