Optimizing WorldView-2, -3 cloud masking using machine learning approaches

Caraballo-Vega, J.A.; Carroll, M.; Neigh, C. S. R.; Wooten, M.; Lee, B.; Weis, A.; Aronne, Mary E.; Alemu, W.G.; Williams, Z.

doi:10.1016/j.rse.2022.113332

Cited by 23 publications

(10 citation statements)

References 45 publications

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“…There are also methods utilizing, as post-processing algorithms, the conditional random field (CRF)-based method to improve boundaries via local features and distance information. The authors in [106] applied a slightly modified version of the U-Net architecture to obtain cloud detection masks for very-high-resolution images. Manually annotated images over three regions were used to train and validate the predicted cloud/non-cloud masks.…”

Section: Deep Learning For Cloud Maskingmentioning

confidence: 99%

An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications

Anzalone,

Pagliaro,

Tutone

2024

Applied Sciences

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Cloud cover assessment is crucial for meteorology, Earth observation, and environmental monitoring, providing valuable data for weather forecasting, climate modeling, and remote sensing activities. Depending on the specific purpose, identifying and accounting for pixels affected by clouds is essential in spectral remote sensing imagery. In applications such as land monitoring and various remote sensing activities, detecting/removing cloud-contaminated pixels is crucial to ensuring the accuracy of advanced processing of satellite imagery. Typically, the objective of cloud masking is to produce an image where every pixel in a satellite spectral image is categorized as either clear or cloudy. Nevertheless, there is also a prevalent approach in the literature that yields a multi-class output. With the progress in Machine and Deep Learning, coupled with the accelerated capabilities of GPUs, and the abundance of available remote sensing data, novel opportunities and methods for cloud detection have emerged, improving the accuracy and the efficiency of the algorithms. This paper provides a review of these last methods for cloud masking in multispectral satellite imagery, with emphasis on the Deep Learning approach, highlighting their benefits and challenges.

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Section: Deep Learning For Cloud Maskingmentioning

confidence: 99%

An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications

Anzalone,

Pagliaro,

Tutone

2024

Applied Sciences

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“…[140] use a RF classification applied to WorldView-2 imagery to identify tree species in the forests of Austria at high resolution. WorldView data are used heavily as the data source for classifications by the land-cover/land-use and the vegetation ecology communities (e.g., [93,94,96,[140][141][142][143][144][145][146][147]).…”

Section: Data Sources and Processingmentioning

confidence: 99%

Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

Herzfeld,

Hessburg,

Trantow

et al. 2024

Remote Sensing

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The objectives of this paper are to investigate the trade-offs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach (“VarioCNN”). Our solution is provided in the framework of a cyberinfrastructure that includes a +newly designed ML software, GEOCLASS-image (v1.0), modern high-resolution satellite image data sets (Maxar WorldView data), and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100–200 times its normal velocity. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in six simplified classes.

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“…Wenkang Liu is with the Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an 710071, China (e-mail: wkliu@stu.xidian.edu.cn). and especially change detection [1,2].…”

Section: Index Terms-syntheticmentioning

confidence: 99%

“…For the three groups of feature maps in Fig. 8 with sizes of 256× 256, 128×128 and 64×64, the kernel dimension s and slice dimension S are set to [1,3,5] and [1,4,8], respectively. Utilizing the above settings, the global context can be captured while maintaining a low computational complexity.…”

Section: ) Group Spatial Convolution For the Message Passing Of The F...mentioning

confidence: 99%

Joint Exploitation of Coherent Change Detection and Global-Context Capturing Network for Subtle Changed Track Detection With Airborne SAR

Zhang,

Xing,

Liu

et al. 2024

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

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Optimizing WorldView-2, -3 cloud masking using machine learning approaches

Cited by 23 publications

References 45 publications

An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications

An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications

Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

Joint Exploitation of Coherent Change Detection and Global-Context Capturing Network for Subtle Changed Track Detection With Airborne SAR

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