A Survey of GPU Implementations for Hyperspectral Image Classification in Remote Sensing

Yusuf, Ayomide; Alawneh, Shadi

doi:10.1080/07038992.2018.1559725

Cited by 17 publications

(10 citation statements)

References 60 publications

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“…(P. Liu et al, 2014). 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.…”

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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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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“…cent studies, CapsNets are increasingly involved in the human-safety related tasks, and on average outperform CNNs by 19.6% and 42.06% on detection accuracy for medical image processing [15,16,17,18,19,20] and autonomous driving [21,22,23]. Because CapsNets execution exhibits a high percentage of matrix operations, state-of-the-art GPUs have become primary platforms for accelerating CapsNets by leveraging their massive on-chip parallelism and deeply optimized software library [24,25]. However, processing efficiency of CapsNets on GPUs often cannot achieve the desired level for fast real-time inference.…”

Section: Cnn Identificationmentioning

confidence: 99%

Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design

Zhang

Song

Xie

et al. 2020

2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)

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In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classification is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to significantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low efficiency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive synchronizations, which are very difficult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named PIM-CapsNet. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's inefficiency by leveraging the processing-in-memory capability of today's 3D stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference efficiency through minimizing data movement and maximizing parallel processing in memory. Evaluation results demonstrate that our proposed design can achieve substantial improvement on both performance and energy savings for CapsNet inference, with almost zero accuracy loss. The results also suggest good performance scalability in optimizing the routing procedure with increasing network size.

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“…Thanks to recent advances in highperformance computing techniques, accurate and efficient classification performance can be achieved for adopted classifiers by exploiting specialized devices such as clusters and distributed computers, multicore CPUs, fieldprogrammable gate arrays (FPGAs), GPUs in hyperspectral image processing [35][36]. Specifically, it is possible to greatly accelerate the computational efficiency of a classifier on a GPU-based parallel computing platform by benefiting from its capacity of performing many computationally intensive tasks in parallel [36][37][38]. Once the computational complexity of the adopted classifier is greatly accelerated, it is also possible to further boost the classification accuracy by constructing an EL system [36][37]39].…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, it is possible to greatly accelerate the computational efficiency of a classifier on a GPU-based parallel computing platform by benefiting from its capacity of performing many computationally intensive tasks in parallel [36][37][38]. Once the computational complexity of the adopted classifier is greatly accelerated, it is also possible to further boost the classification accuracy by constructing an EL system [36][37]39]. Hence, it is of interest to investigate the performance of the GPUaccelerated CatBoost (GPU-CatBoost) algorithm and its ensemble version in hyperspectral image classification using diverse features.…”

Section: Introductionmentioning

confidence: 99%

GPU-Accelerated CatBoost-Forest for Hyperspectral Image Classification Via Parallelized mRMR Ensemble Subspace Feature Selection

Samat

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this article, the graphics processing unit (GPU)accelerated CatBoost (GPU-CatBoost) algorithm for hyperspectral image classification is first introduced and comparatively studied using diverse features. To further foster the classification performance from both accurate and efficient viewpoints, an ensemble version of GPU-CatBoost, the GPUaccelerated CatBoost-Forest (GPU-CatBF) algorithm, is proposed by adopting the parallelized minimum redundancy maximum relevance (mRMR) ensemble (PmRMRE) feature selection (FS) algorithm. To evaluate the performance and suitability of mRMR and PmRMRE, eleven other state-of-theart FS algorithms are comprehensively investigated. Experimental results on three widely acknowledged hyperspectral benchmarks showed that 1) GPU-CatBoost is also an advanced ensemble learning (EL) algorithm for hyperspectral image classification using diverse features; 2) mRMR and PmRMRE have advanced properties for highly discriminative features and band selection, and the best results are achieved by PmRMRE in the most cases in terms of both the robustness and computational efficiency; and 3) GPU-CatBF always outperforms CatBoost and GPU-CatBoost, while compatible and even better results are reachable without losing much computational efficiency in contrast with other selected decision tree (DT)-based EL algorithms.

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A Survey of GPU Implementations for Hyperspectral Image Classification in Remote Sensing

Cited by 17 publications

References 60 publications

Gpu Utilization in Geoprocessing Big Geodata: A Review

Gpu Utilization in Geoprocessing Big Geodata: A Review

Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design

GPU-Accelerated CatBoost-Forest for Hyperspectral Image Classification Via Parallelized mRMR Ensemble Subspace Feature Selection

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