Comparative Analysis of the Classification of Maximum Reality (MVS) and the Spectral Angle Mapper (SAM) of an Aster Image. Case Study: Soil Occupancy in the Main Area (Tunisia)

2019 ◽  
pp. 51-62
Author(s):  
Sonia Gannouni ◽  
Noamen Rebai
Keyword(s):  
Comparative Analysis ◽  
Spectral Angle Mapper ◽  
Main Area ◽  
Aster Image ◽  
Spectral Angle

scholarly journals Centenarian “Weird Fiction” from Providence in Contemporary Poland. Translational Models and the Reception of Short Stories by Howard Phillips Lovecraft – the Case Study of “Dagon”

2020 ◽  
Vol nr specjalny 1(2020) ◽  
pp. 311-334
Author(s):  
Arkadiusz Luboń ◽  
Keyword(s):  
Short Stories ◽  
Comparative Analysis ◽  
Weird Fiction ◽  
Translation Techniques ◽  
Conventional Models ◽  
American Author ◽  
The Impact ◽  
Translational Models

The article discusses the conventional models and translation techniques, which are most common among the Polish translators of the weird fiction by Howard Phillips Lovecraft. The proposed classification of such models, aimed at either “popularisation,” “stereotypisation” or “revision” of Lovecraft’s short stories, presents the impact of extra-textual factors (vision of the writer, target group of readers, cultural and political influences) on the content, language and style of translated works by the American author. The comparative analysis takes into consideration one of the early short stories by Lovecraft, “Dagon” (1917), and its Polish versions by Arnold Mostowicz (1973), Robert Lipski (1994) and Maciej Płaza (2012).

Contaminant Classification of Poultry Hyperspectral Imagery using a Spectral Angle Mapper Algorithm

2007 ◽  
Vol 96 (3) ◽  
pp. 323-333 ◽  
Author(s):  
B. Park ◽  
W.R. Windham ◽  
K.C. Lawrence ◽  
D.P. Smith
Keyword(s):  
Hyperspectral Imagery ◽  
Spectral Angle Mapper ◽  
Spectral Angle

scholarly journals Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

2019 ◽  
Vol 11 (9) ◽  
pp. 1136 ◽  
Author(s):  
Muhammad Ahmad ◽  
Asad Khan ◽  
Adil Mehmood Khan ◽  
Manuel Mazzara ◽  
Salvatore Distefano ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Sample Selection ◽  
Spectral Angle Mapper ◽  
Classifier Design ◽  
Heterogeneous Samples ◽  
Marginal Probability Distribution ◽  
Benchmark Datasets ◽  
Manual Selection ◽  
Spectral Angle

Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements.

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