scholarly journals Monitoring Mount Sinabung in Indonesia Using Multi-Temporal InSAR

2017 ◽  
Vol 33 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Chang-Wook Lee ◽  
Zhong Lu ◽  
Jin Woo Kim
Keyword(s):  
Multi Temporal

The Relationship Between Leader–Member Exchange and Employees’ Proactive Behaviors

2019 ◽  
Vol 18 (2) ◽  
pp. 106-111
Author(s):  
Fong-Yi Lai ◽  
Szu-Chi Lu ◽  
Cheng-Chen Lin ◽  
Yu-Chin Lee
Keyword(s):  
Hierarchical Linear Modeling ◽  
Future Research ◽  
Leader Member Exchange ◽  
Voice Behavior ◽  
Linear Modeling ◽  
Team Members ◽  
Member Exchange ◽  
Multi Temporal ◽  
The Relationship ◽  
Practical Implications

Abstract. The present study proposed that, unlike prior leader–member exchange (LMX) research which often implicitly assumed that each leader develops equal-quality relationships with their supervisors (leader’s LMX; LLX), every leader develops different relationships with their supervisors and, in turn, receive different amounts of resources. Moreover, these differentiated relationships with superiors will influence how leader–member relationship quality affects team members’ voice and creativity. We adopted a multi-temporal (three wave) and multi-source (leaders and employees) research design. Hypotheses were tested on a sample of 227 bank employees working in 52 departments. Results of the hierarchical linear modeling (HLM) analysis showed that LLX moderates the relationship between LMX and team members’ voice behavior and creative performance. Strengths, limitations, practical implications, and directions for future research are discussed.

Multi-temporal Hyperspectral Images Unmixing and Classification Based on 3D Signature Model and Matching

PIERS Online ◽  
2010 ◽  
Vol 6 (5) ◽  
pp. 480-484 ◽  
Author(s):  
Imed Riadh Farah ◽  
Selim Hemissi ◽  
Karim Saheb Ettabaa ◽  
Bassel Souleiman
Keyword(s):  
Hyperspectral Images ◽  
Multi Temporal

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

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