Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines

Miroszewski, Artur; Mielczarek, Jakub; Czelusta, Grzegorz; Filip, Szczepanek,; Grabowski, Bartosz; Saux, Bertrand Le; Nalepa, Jakub

doi:10.1109/jstars.2023.3304122

Cited by 12 publications

(2 citation statements)

References 40 publications

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“…From the perspective of computational time required and electrical power consumed, VQAs require less training datasets compared to conventional DL models [39] -it implies faster training time than its counterpart classical technique, whereas quantum machines also consume less electric power than supercomputers at the same time [2] (e.g., a D-Wave quantum annealer operates at around 25 kW power, whereas the Summit supercomputer consumes around 13 MW power). VQAs are already applied to, for example, change detection [40,41], chlorophyll concentration estimation in water [42], detecting clouds [43], and phase unwrapping for synthetic aperture radar datasets [44,45]. 2.…”

Section: Quantum For Climate Change Detectionmentioning

confidence: 99%

Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin

Otgonbaatar,

Nurmi,

Johansson

et al. 2023

Preprint

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<p>This study explores the potential of quantum machine learning and quantum computing for climate change detection, climate modeling, and climate digital twin. We additionally consider the time and energy consumption of quantum machines and classical computers. Moreover, we identified several use-case instances for climate change detection, climate modeling, and climate digital twin that are challenging for conventional computers but can be tackled efficiently with quantum machines or by integrating them with classical computers. We also evaluated the efficacy of quantum annealers, quantum simulators, and universal quantum computers, each designed to solve specific types and kinds of computational problems that are otherwise difficult.</p>

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Section: Quantum For Climate Change Detectionmentioning

confidence: 99%

Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin

Otgonbaatar,

Nurmi,

Johansson

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…When scaling features, the phenomenon that the information loss caused by multiple down-sampling is effectively reduced. It extracts multi-scale features of images without losing information and combines residual units to make the network At present, cloud detection and segmentation approaches are mainly bifurcated into two parts: threshold-based approaches and learning-based approaches [1][2][3][4]. The first type of algorithm is mainly based on cloud spectral characteristics (portions of the electromagnetic spectrum), brightness, texture characteristics and geometry by analyzing the spectral difference between the cloud and other surfaces, thresholds or rules are formulated to realize cloud extraction [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

MSACN: A Cloud Extraction Method from Satellite Image Using Multiscale Soft Attention Convolutional Neural Network

Gao,

Gai,

et al. 2024

Applied Sciences

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In satellite remote sensing images, the existence of clouds has an occlusion effect on ground information. Different degrees of clouds make it difficult for existing models to accurately detect clouds in images due to complex scenes. The detection and extraction of clouds is one of the most important problems to be solved in the further analysis and utilization of image information. In this article, we refined a multi-head soft attention convolutional neural network incorporating spatial information modeling (MSACN). During the encoder process, MSACN extracts cloud features through a concurrent dilated residual convolution module. In the part of the decoder, there is an aggregating feature module that uses a soft attention mechanism. It integrates the semantic information with spatial information to obtain the pixel-level semantic segmentation outputs. To assess the applicability of MSACN, we compare our network with Transform-based and other traditional CNN-based methods on the ZY-3 dataset. Experimental outputs including the other two datasets show that MSACN has a better overall performance for cloud extraction tasks, with an overall accuracy of 98.57%, a precision of 97.61%, a recall of 97.37%, and F1-score of 97.48% and an IOU of 95.10%.

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CSDUNet: Automatic Cloud and Shadow Detection from Satellite Images Based on Modified U-Net

Surya,

Abdul Rahiman

2024

J Indian Soc Remote Sens

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Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines

Cited by 12 publications

References 40 publications

Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin

Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin

MSACN: A Cloud Extraction Method from Satellite Image Using Multiscale Soft Attention Convolutional Neural Network

CSDUNet: Automatic Cloud and Shadow Detection from Satellite Images Based on Modified U-Net

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