178,814 research outputs found
A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest
Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way
A Learning Algorithm based on High School Teaching Wisdom
A learning algorithm based on primary school teaching and learning is
presented. The methodology is to continuously evaluate a student and to give
them training on the examples for which they repeatedly fail, until, they can
correctly answer all types of questions. This incremental learning procedure
produces better learning curves by demanding the student to optimally dedicate
their learning time on the failed examples. When used in machine learning, the
algorithm is found to train a machine on a data with maximum variance in the
feature space so that the generalization ability of the network improves. The
algorithm has interesting applications in data mining, model evaluations and
rare objects discovery
A review of associative classification mining
Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper
Metamodel-based importance sampling for the simulation of rare events
In the field of structural reliability, the Monte-Carlo estimator is
considered as the reference probability estimator. However, it is still
untractable for real engineering cases since it requires a high number of runs
of the model. In order to reduce the number of computer experiments, many other
approaches known as reliability methods have been proposed. A certain approach
consists in replacing the original experiment by a surrogate which is much
faster to evaluate. Nevertheless, it is often difficult (or even impossible) to
quantify the error made by this substitution. In this paper an alternative
approach is developed. It takes advantage of the kriging meta-modeling and
importance sampling techniques. The proposed alternative estimator is finally
applied to a finite element based structural reliability analysis.Comment: 8 pages, 3 figures, 1 table. Preprint submitted to ICASP11
Mini-symposia entitled "Meta-models/surrogate models for uncertainty
propagation, sensitivity and reliability analysis
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