GPU-Card Performance Research in Satellite Imagery Classification Problems Using Machine Learning

Sharma, Dilip Kumar; Malikov, Vitaliy; Parygin, Danila; Golubev, Alexey; Lozhenitsina, Anastasia; Sadovnikova, Natalia

doi:10.1016/j.procs.2020.11.007

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Recently, the performances of CNNs have been significantly improved [19][20][21][22][23][24][25]. When using this type of neural network in combination with powerful graphics-processing units [26], the CNN is the key technology behind new developments in driverless driving and facial recognition. However, as the authors of [27] note, convolutional neural networks work very slowly with high-resolution images and on devices with weak processors.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

et al. 2022

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The sustainable functioning of the transport system requires solving the problems of identifying and classifying road users in order to predict the likelihood of accidents and prevent abnormal or emergency situations. The emergence of unmanned vehicles on urban highways significantly increases the risks of such events. To improve road safety, intelligent transport systems, embedded computer vision systems, video surveillance systems, and photo radar systems are used. The main problem is the recognition and classification of objects and critical events in difficult weather conditions. For example, water drops, snow, dust, and dirt on camera lenses make images less accurate in object identification, license plate recognition, vehicle trajectory detection, etc. Part of the image is overlapped, distorted, or blurred. The article proposes a way to improve the accuracy of object identification by using the Canny operator to exclude the damaged areas of the image from consideration by capturing the clear parts of objects and ignoring the blurry ones. Only those parts of the image where this operator has detected the boundaries of the objects are subjected to further processing. To classify images by the remaining whole parts, we propose using a combined approach that includes the histogram-oriented gradient (HOG) method, a bag-of-visual-words (BoVW), and a back propagation neural network (BPNN). For the binary classification of the images of the damaged objects, this method showed a significant advantage over the classical method of convolutional neural networks (CNNs) (79 and 65% accuracies, respectively). The article also presents the results of a multiclass classification of the recognition objects on the basis of the damaged images, with an accuracy spread of 71 to 86%.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

et al. 2022

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“…Previously, the utilization GPU functions included satellite imageries classification (Sharma et al, 2020), real-time radiometric correction (Fang et al, 2014), soil parameter inversion (Yin et al, 2020), noise removal (Granata et al, 2020) and hyperspectral image classification (Yusuf & Alawneh, 2018). Some of these applications are being optimised using NVIDIA's application programming interface (API), Compute Unified Device Architecture (CUDA) (Fang et al, 2014;Sharma et al, 2020;Yin et al, 2020) and OpenCL (Granata et al, 2020), an open-source API used for NVIDIA or AMD manufactured GPU. These studies displayed satellite image processing able to demonstrate a good flexibility to GPU computational elements.…”

Section: Remote Sensingmentioning

confidence: 99%

“…Using GPU to accelerate satellite image processing has a huge influence on the remote sensing industry. Sharma et al, (2020) investigated the feasibility of employing GPU to accelerate batch processing of spatial raster data. They concluded that the GPU is capable of drawing conclusions about its applicability in solving various problems related to geoinformation and its efficiency processes by using neutral network training for segmenting images of 10 classes that included ground, non-ground, and manmade features of 1601 images.…”

Section: Remote Sensingmentioning

confidence: 99%

Gpu Utilization in Geoprocessing Big Geodata: A Review

Teri

Musliman

Rahman

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The expansion of data collection from remote sensing and other geographic data sources, as well as from other technology such as cloud, sensors, mobile, and social media, have made mapping and analysis more complex. Some geospatial applications continue to rely on conventional geospatial processing, where limitation on computation capabilities often lacking to attain significant data interpretation. In recent years, GPU processing has improved far more GIS applications than using CPU alone. As a result, numerous researchers have begun utilising GPUs for scientific, geometric, and database computations in addition to graphics hardware use. This paper summarizes parallel processing concept and architecture, the development of GPU geoprocessing for big geodata ranging from remote sensing and 3D modelling to smart cities studies. This paper also addresses the GPU future trends advancement opportunities with other technologies, machine learning, deep learning, and cloud-based computing.

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“…Yet, accelerating applications at the CPU level is still respectable compared to what is required in terms of computing. Therefore, many research fields were benefited from GPU acceleration techniques [ 10 , 14 , 22 , 39 , 40 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

Ali

Abbassi

Bouattane

2022

Multimed Tools Appl

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Image processing by segmentation technique is an important phase in medical imaging such as MRI. Its objective is to analyze the different tissues in human body. In research area, Fuzzy set is one of the most successful techniques that guarantees a robust classification. Spatial FCM (SFCM); one of the fuzzy c-means variants; considers spatial information to deal with the noisy images. To reduce this iterative algorithm’s execution time, a hard SIMD architecture has been planted named the Graphical Processing Unit (GPU). In this work, a great contribution has been done to diagnose, confront and implement three different parallel implementations on GPU. A parallel implementations’ extensive study of SFCM entitled PSFCM using 3 × 3 window is presented, and the experiments illustrate a significant decrease in terms of running time of this algorithm known by its high complexity. The experimental results indicate that the parallel version’s execution time is about 9.46 times faster than the sequential implementation on image segmentation. This gain in terms of speed-up is achieved on the Nvidia GeForce GT 740 m GPU.

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GPU-Card Performance Research in Satellite Imagery Classification Problems Using Machine Learning

Cited by 7 publications

References 9 publications

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

Gpu Utilization in Geoprocessing Big Geodata: A Review

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

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