Noise reduction in remote sensing imagery using data masking and principal component analysis

2000 ◽  
Author(s):  
Brian R. Corner ◽  
Ram M. Narayanan ◽  
Stephen E. Reichenbach
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Noise Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Remote Sensing Imagery ◽  
Data Masking ◽  
Using Data

scholarly journals A phenetic study of the genus Rasahus Amyot & Serville (Heteroptera, Reduviidae)

1990 ◽  
Vol 1 (3) ◽  
pp. 131-144
Author(s):  
María Coscarón
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Morphological Characters ◽  
Basal Plate ◽  
Using Data ◽  
Diagnostic Importance

Cluster analysis by four methods and a principal component analysis were performed using data on 24 morphological characters of 27 species of the genus Rasahus (Peiratinae). The results obtained by the different techniques show general agreement. They confirm the present number of taxa and reveal the existence within the genus of three groups of species: scutellaris , hamatus and vittatus. The scutellaris group is constituted by R. aeneus (Walker), R. maculipennis (Lepelletier and Serville), R. bifurcatas Champion, R. castaneus Coscarón, R. guttatipennis (Stål), R. flavovittarus Stål, R. costarricensis Coscarón, R. scutellaris (Fabricius), R. atratus Coscarón, R. peruensis Coscarón, R. paraguayensis Coscarón, R. surinamensis Coscarón, R. albomaculatus Mayr, R. brasiliensis Coscarón and R. sulcicollis (Serville).The hamatus group contains R. rufiventris (Walker), R. hamatus (Fabricius), R. amapaensis Coscarón, R. arcitenens Stål, R. limai Pinto, R. angulatus coscarón, R. thoracicus Stål, R. biguttatus (Say), R. arcuiger (Stål), R. argentinensis Coscarón and R. grandis Fallou. The vittatus group contains R. vittatus Coscarón. The characters used to separate the groups of species are: shape of the pygophore, shape of the parameres, basal plate complexity, shape of the postocular region and hemelytra pattern. Illustrations of the structures of major diagnostic importance are included.

scholarly journals EVALUATING MBSE POTENTIAL USING PRODUCT AND DEVELOPMENT CHARACTERISTICS – A STATISTICAL INVESTIGATION

2020 ◽  
Vol 1 ◽  
pp. 2385-2394
Author(s):  
M. Schöberl ◽  
E. Rebentisch ◽  
J. Trauer ◽  
M. Mörtl ◽  
J. Fottner
Keyword(s):  
Principal Component Analysis ◽  
Systems Engineering ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Investigation ◽  
Product Complexity ◽  
Organizational Complexity ◽  
Model Based ◽  
Using Data ◽  
Model Based Systems Engineering

AbstractAs model-based systems engineering (MBSE) is evolving, the need for evaluating MBSE approaches grows. Literature shows that there is an untested assertion in the MBSE community that complexity drives the adoption of MBSE. To assess this assertion and support the evaluation of MBSE, a principal component analysis was carried out on eight product and development characteristics using data collected in an MBSE course, resulting in organizational complexity, product complexity and inertia. To conclude, the method developed in this paper enables organisations to evaluate their MBSE adoption potential.

scholarly journals Identification of catchment functional units by time series of thermal remote sensing images

2014 ◽  
Vol 18 (12) ◽  
pp. 5345-5359 ◽  
Author(s):  
B. Müller ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Time Series ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Principal Component ◽  
Component Analysis ◽  
Land Surface Models ◽  
Surface Models ◽  
Functional Units

Abstract. The identification of catchment functional behavior with regards to water and energy balance is an important step during the parameterization of land surface models. An approach based on time series of thermal infrared (TIR) data from remote sensing is developed and investigated to identify land surface functioning as is represented in the temporal dynamics of land surface temperature (LST). For the mesoscale Attert catchment in midwestern Luxembourg, a time series of 28 TIR images from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) was extracted and analyzed, applying a novel process chain. First, the application of mathematical–statistical pattern analysis techniques demonstrated a strong degree of pattern persistency in the data. Dominant LST patterns over a period of 12 years were then extracted by a principal component analysis. Component values of the two most dominant components could be related for each land surface pixel to land use data and geology, respectively. The application of a data condensation technique ("binary words") extracting distinct differences in the LST dynamics allowed the separation into landscape units that show similar behavior under radiation-driven conditions. It is further outlined that both information component values from principal component analysis (PCA), as well as the functional units from the binary words classification, will highly improve the conceptualization and parameterization of land surface models and the planning of observational networks within a catchment.

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