Automatic contrail detection and segmentation

Weiss, John M.; Christopher, S. A.; Welch, Ronald M.

doi:10.1109/36.718864

Cited by 16 publications

(5 citation statements)

References 14 publications

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“…Researching global cloud and aerosol properties, radiative energy balance, 3D cloud morphology, and infectious disease risk due to climate fluctuations highlighted the importance of studying contrails’ effects on radiative balance and cloud formation in various contexts. The proposed multistep protocol for contrail detection and segmentation in AVHRR images shows promise in identifying contrail properties yet acknowledges challenges in detecting certain types and improving algorithm precision [ 15 ]. The study also revealed that converting the RGB color space to HSV enhanced segmenting satellite images, indicating the practical utility of color space transformations in this context [ 16 ].…”

Section: Related Workmentioning

confidence: 99%

SegX-Net: A novel image segmentation approach for contrail detection using deep learning

Nobel,

Hossain,

Kabir

et al. 2024

PLoS ONE

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Contrails are line-shaped clouds formed in the exhaust of aircraft engines that significantly contribute to global warming. This paper confidently proposes integrating advanced image segmentation techniques to identify and monitor aircraft contrails to address the challenges associated with climate change. We propose the SegX-Net architecture, a highly efficient and lightweight model that combines the DeepLabV3+, upgraded, and ResNet-101 architectures to achieve superior segmentation accuracy. We evaluated the performance of our model on a comprehensive dataset from Google research and rigorously measured its efficacy with metrics such as IoU, F1 score, Sensitivity and Dice Coefficient. Our results demonstrate that our enhancements have significantly improved the efficacy of the SegX-Net model, with an outstanding IoU score of 98.86% and an impressive F1 score of 99.47%. These results unequivocally demonstrate the potential of image segmentation methods to effectively address and mitigate the impact of air conflict on global warming. Using our proposed SegX-Net architecture, stakeholders in the aviation industry can confidently monitor and mitigate the impact of aircraft shrinkage on the environment, significantly contributing to the global fight against climate change.

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Section: Related Workmentioning

confidence: 99%

SegX-Net: A novel image segmentation approach for contrail detection using deep learning

Nobel,

Hossain,

Kabir

et al. 2024

PLoS ONE

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“…The analysis of clouds and their features is important for a wide variety of applications. It has been used for now casting to deliver accurate weather forecasts [14], rainfall, and satellite precipitation estimates [15], in the study of contrails [21], and various other day-to-day meteorological applications [7,16]. For cloud cover estimation, cloud detection plays a vital role, which classifies each pixel of all-sky images into clear sky element or cloud.…”

Section: Feature Extraction and Image Segmentationmentioning

confidence: 99%

Intelligent Methods Used for Obtaining Weather Derivatives: A Review

Rudrappa¹,

Vijapur²

2019

EAS

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Weather is the condition of the atmosphere and forecasting is predicting the condition of the atmosphere in near future. Weather forecasting is a formidable challenge as weather is a multi-dimensional, continuous and chaotic process. The distinct nature of the model forecasting in all situations accurately is challenging. Presently weather conditions are being obtained from satellites, Doppler radar, radio sounds, observations from aircraft and ground. Collected data is subjected to various statistical and machine learning techniques. These techniques can incorporate relatively simple observation of the sky to highly complex computerized mathematical models. Weather forecasting still remains a challenging issue, due to unpredictable and chaotic nature of weather. Even with present methods the weather forecasting system may still fail to predict weather attribute, therefore there is still scope left to improve these systems. The objective of carrying out the survey is to look forward on how machine learning can help us to improve weather parameter estimation. In this paper we report the different methods carried out by leading researchers, formidable challenges and present our views on development of efficient weather forecasting system. Also, we propose a method which makes use image processing and neural networks to achieve the weather parameter estimation.

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“…Skeletons of linear structures are an important representation in the human visual systems. Ridge detection (also known as linear delineation [5]) has applications in medical imaging [5], remote sensing [20], photogrammetry [4], and other areas.…”

Section: Thinningmentioning

confidence: 99%

Efficient grayscale thinning on parallel hardware

Weiss¹,

Karlsson²

2017

JCM

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Thinning is an important task in many image processing applications, including remote sensing, photogrammetry, optical character recognition, and medical imaging. In this study, we compare the performance of thinning algorithms on parallel hardware. Grayscale thinning involves a substantial amount of computation per pixel, and may be accelerated in several ways: algorithmic improvements, code optimization, and parallelization. We describe an algorithmic improvement that speeds up grayscale thinning several-fold, and demonstrate scalable acceleration from multi-core CPU concurrency libraries (such as OpenMP), coprocessor hardware (such as the Xeon Phi), and GPUs (such as CUDA-enabled NVIDIA graphics cards). GPU processing appears to offer the most cost-effective approach for high performance grayscale thinning applications.

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Automatic contrail detection and segmentation

Cited by 16 publications

References 14 publications

SegX-Net: A novel image segmentation approach for contrail detection using deep learning

SegX-Net: A novel image segmentation approach for contrail detection using deep learning

Intelligent Methods Used for Obtaining Weather Derivatives: A Review

Efficient grayscale thinning on parallel hardware

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