Wavelet-Based Classification of Hyperspectral Images Using Extended Morphological Profiles on Graphics Processing Units

2015 ◽  
Vol 8 (6) ◽  
pp. 2962-2970 ◽  
Author(s):  
Pablo Quesada-Barriuso ◽  
Francisco Arguello ◽  
Dora B. Heras ◽  
Jon Atli Benediktsson
Keyword(s):  
Graphics Processing Units ◽  
Hyperspectral Images ◽  
Graphics Processing

scholarly journals Towards a Unified Sentiment Lexicon Based on Graphics Processing Units

2014 ◽  
Vol 2014 ◽  
pp. 1-19
Author(s):  
Liliana Ibeth Barbosa-Santillán ◽  
Inmaculada Álvarez-de-Mon y-Rego
Keyword(s):  
Graphics Processing Units ◽  
Pearson Correlation ◽  
Lexical Entry ◽  
A Value ◽  
Sentiment Lexicon ◽  
Unit Of Measurement ◽  
Time Required ◽  
Graphics Processing ◽  
High Processing

This paper presents an approach to create what we have called a Unified Sentiment Lexicon (USL). This approach aims at aligning, unifying, and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. One problem related to the task of the automatic unification of different scores of sentiment lexicons is that there are multiple lexical entries for which the classification of positive, negative, or neutral{P,N,Z}depends on the unit of measurement used in the annotation methodology of the source sentiment lexicon. Our USL approach computes the unified strength of polarity of each lexical entry based on the Pearson correlation coefficient which measures how correlated lexical entries are with a value between 1 and −1, where 1 indicates that the lexical entries are perfectly correlated, 0 indicates no correlation, and −1 means they are perfectly inversely correlated and so is the UnifiedMetrics procedure for CPU and GPU, respectively. Another problem is the high processing time required for computing all the lexical entries in the unification task. Thus, the USL approach computes a subset of lexical entries in each of the 1344 GPU cores and uses parallel processing in order to unify 155802 lexical entries. The results of the analysis conducted using the USL approach show that the USL has 95.430 lexical entries, out of which there are 35.201 considered to be positive, 22.029 negative, and 38.200 neutral. Finally, the runtime was 10 minutes for 95.430 lexical entries; this allows a reduction of the time computing for the UnifiedMetrics by 3 times.

scholarly journals The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video

2020 ◽  
Vol 12 (18) ◽  
pp. 3020
Author(s):  
Piotr Szymak ◽  
Paweł Piskur ◽  
Krzysztof Naus
Keyword(s):  
Deep Learning ◽  
World War Ii ◽  
Graphics Processing Units ◽  
Object Classification ◽  
Underwater Vehicles ◽  
Training Methods ◽  
Underwater Video ◽  
Deep Learning Neural Network ◽  
Graphics Processing

Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment.

scholarly journals PARALLEL IMPLEMENTATION OF MORPHOLOGICAL PROFILE BASED SPECTRAL-SPATIAL CLASSIFICATION SCHEME FOR HYPERSPECTRAL IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 263-267
Author(s):  
B. Kumar ◽  
O. Dikshit
Keyword(s):  
Classification Accuracy ◽  
Graphics Processing Units ◽  
Spatial Information ◽  
Parallel Implementation ◽  
Hyperspectral Imagery ◽  
Hyperspectral Images ◽  
Important Concern ◽  
Speed Up ◽  
Graphics Processing ◽  
The Impact

Extended morphological profile (EMP) is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. However, with the availability of modern multi-core processors and commodity parallel processing systems like graphics processing units (GPUs) at desktop level, parallel computing provides a viable option to significantly accelerate execution of such computations. In this paper, parallel implementation of an EMP based spectralspatial classification method for hyperspectral imagery is presented. The parallel implementation is done both on multi-core CPU and GPU. The impact of parallelization on speed up and classification accuracy is analyzed. For GPU, the implementation is done in compute unified device architecture (CUDA) C. The experiments are carried out on two well-known hyperspectral images. It is observed from the experimental results that GPU implementation provides a speed up of about 7 times, while parallel implementation on multi-core CPU resulted in speed up of about 3 times. It is also observed that parallel implementation has no adverse impact on the classification accuracy.

scholarly journals PARALLEL IMPLEMENTATION OF MORPHOLOGICAL PROFILE BASED SPECTRAL-SPATIAL CLASSIFICATION SCHEME FOR HYPERSPECTRAL IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 263-267
Author(s):  
B. Kumar ◽  
O. Dikshit
Keyword(s):  
Classification Accuracy ◽  
Graphics Processing Units ◽  
Spatial Information ◽  
Parallel Implementation ◽  
Hyperspectral Imagery ◽  
Hyperspectral Images ◽  
Important Concern ◽  
Speed Up ◽  
Graphics Processing ◽  
The Impact

Extended morphological profile (EMP) is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. However, with the availability of modern multi-core processors and commodity parallel processing systems like graphics processing units (GPUs) at desktop level, parallel computing provides a viable option to significantly accelerate execution of such computations. In this paper, parallel implementation of an EMP based spectralspatial classification method for hyperspectral imagery is presented. The parallel implementation is done both on multi-core CPU and GPU. The impact of parallelization on speed up and classification accuracy is analyzed. For GPU, the implementation is done in compute unified device architecture (CUDA) C. The experiments are carried out on two well-known hyperspectral images. It is observed from the experimental results that GPU implementation provides a speed up of about 7 times, while parallel implementation on multi-core CPU resulted in speed up of about 3 times. It is also observed that parallel implementation has no adverse impact on the classification accuracy.

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