Segmentation of Satellite Imagery Using Hierarchical Thresholding and Neural Networks

Peak, James E.; Tag, Paul M.

doi:10.1175/1520-0450(1994)033<0605:sosiuh>2.0.co;2

Cited by 51 publications

(22 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The interpretation and visual classification of cloud types is done manually by experts. This process is significantly dependent on the experience of the person who performs the analysis, requiring time, training and personal For automatic detection of convective system, based on Chetan, Peak, Desbois and Azimi-Sadjadi [10], [27]- [29], artificial intelligence (AI) and digital image processing (DIP) techniques are used typically to automate the meteorological pattern recognition. Other authors, like Yin and Qian, [7], have worked with unsupervised methods using propose intelligent segmentation techniques as a recognition method.…”

Section: Detection Of Dangerous Meteorological Phenomena For Aviationmentioning

confidence: 99%

Técnicas inteligentes para la identificación y el seguimiento de fenómenos meteorológicos que podrían afectar la seguridad de vuelo

Zuluaga

Vargas²,

Reina³

2017

Ciencia y Poder Aéreo

View full text Add to dashboard Cite

Resumo: Na aviação, os fenômenos meteorológicos são um dos aspectos mais importantes a ter em conta em todas as etapas do vôo, do planejamento ao pouso. O desenvolvimento de sistemas de previsão do tempo aplicados à aviação pode apoiar o processo de tomada de decisão de controladores e pilotos de tráfego aéreo, facilitando a análise de variáveis meteorológicas e fornecendo uma primeira interpretação disponível para todos os usuários do sistema de ar . Por esta razão, o Centro de Desenvolvimento Tecnológico para a Defesa (CETAD) tem como objetivo principal neste documento descrever os resultados do desenvolvimento de uma metodologia sistematizada que utiliza técnicas inteligentes para a detecção, identificação e monitoramento de qualquer tipo de treinamento que, devido às suas características pode representar um risco para a aviação, gerando, por sua vez, informações de suporte para o controlador de tráfego aéreo. Para isso, é necessário primeiro identificar as formações convectivas, classificá-las, filtrar o ruído e individualizá-las. Este tipo de processo pode ser automatizado através da análise inteligente Resumen: en la aviación, los fenómenos meteorológicos son uno de los aspectos más importantes para tener en cuenta en todas las etapas de vuelo, desde la planificación hasta el aterrizaje. El desarrollo de sistemas de predicción meteorológica aplicados a la aviación puede apoyar el proceso de toma de decisiones de los controladores de tráfico aéreo y los pilotos, facilitando el análisis de las variables meteorológicas y proporcionando una primera interpretación a disposición de todos los usuarios del sistema aéreo. Por esta razón el Centro de desarrollo Tecnológico para la Defensa (CETAD) tiene como principal objetivo en este documento describir los resultados del desarrollo de una metodología sistematizada que utiliza técnicas inteligentes para la detección, identificación y seguimiento de cualquier tipo de formación que por sus características pueda representar un riesgo para la aviación, generando a su vez información de soporte al controlador aéreo. Para esto es necesario primero identificar las formaciones convectivas, clasificarlas, filtrar el ruido e individualizarlas. Este tipo de procesos pueden ser automatizados a través del análisis inteligente de productos disponibles en cualquier sistema aéreo como las imágenes satelitales multiespectrales. Posterior a una identificación, se deben determinar un grupo de características que permitan desarrollar algoritmos eficientes capaces de realizar un seguimiento del comportamiento de la formación convectiva, que permita generar pronósticos de las características de los sistemas convectivos en el corto plazo, para lo que se requiere conocer otras variables como el viento en las áreas de análisis. Este tipo de aplicaciones integradas a los sistemas de control de tráfico aérea disminuirían los riesgos debidos factores meteorológicos.Palabras clave: sistemas de control de aire, análisis meteorológico, inteligencia artificial, imá-genes satelitales, sistemas de p...

show abstract

Section: Detection Of Dangerous Meteorological Phenomena For Aviationmentioning

confidence: 99%

Técnicas inteligentes para la identificación y el seguimiento de fenómenos meteorológicos que podrían afectar la seguridad de vuelo

Zuluaga

Vargas²,

Reina³

2017

Ciencia y Poder Aéreo

View full text Add to dashboard Cite

show abstract

“…When clouds are observed by radiometers on-board satellites, they present a relatively high reflectivity in the visible and nearinfrared bands and low Brightness Temperatures (BT) in the thermal infrared bands (Peak, et al, 1994). Around 0.936um band, with the impact of the absorption by water vapour, the clouds reflectance is on the absorption valley.…”

Section: Theorymentioning

confidence: 99%

A Dynamic Threshold Cloud Detecting Approach based on the Brightness Temperature from FY-2 VISSR Data

Daxiang

Tan

Xiongfei

et al. 2015

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

View full text Add to dashboard Cite

THEME: DATA -Data and information systems and spatial data infrastructure.KEY WORDS: Cloud detecting, Brightness temperature, FY-2 VISSR data, Dynamic threshold, Temporal-Spatial scale ABSTRACT:The traditional statistical methods and radiation transfer theory methods for cloud detecting have a high adaptability just only in those areas with a uniform surface coverage and noncomplex terrain. Therefore, adapted to large spatial and temporal scales, in this work a cloud detection method is developed, seeking the main influencing factors of the change of Brightness Temperature(BT) of clear sky and their relationships, researching the change regularity and normal fluctuation range of BT on the basis of function fitting, setting the cloud detecting dynamic threshold depending on the cloud spectral characteristics, and making accuracy assessment in order to ensure higher adaptability and accuracy of this cloud detecting method. In this paper, a dynamic threshold algorithm is presented for cloud detection using daytime imagery from the VISSR sensor on board FY-2C/D/E, which is the first generation geostationary satellite. And the land surface/brightness temperature influence functions are analysis and established, including latitude, longitude, altitude, time, land cover. The theoretical temperature value of clear sky can be calculated through these influence functions. Then, the dynamic threshold cloud detection model is proposed based on the high temporal resolution of VISSR data. Meanwhile, the land surface emissivity is considered as the main factor to the change range of brightness temperature which determines the dynamic threshold for cloud detection. Finally, the dynamic threshold cloud detecting model is evaluated using FY-2C/D/E VISSR data covering China, and the Kappa of dynamic method is maximum, equalling 0.6195, which is much higher than the indexes for the reflectivity and BT fixed methods, equalling 0.4511 and 0.403, respectively. Consequently, the dynamic threshold cloud detecting method provides an important improvement because the spatial, temporal and geographic characteristics were considered into the model.

show abstract

“…An alternative approach not used here is hierarchical thresholding segmentation (Peak and Tag 1994). This method has been shown to be very eOE ective in relatively Downloaded by [Florida State University] at 08:59 01 December 2014 simple situations such as isolated cloud systems over ocean.…”

Section: Image Segmentationmentioning

confidence: 99%

Neural network based methods for cloud classification on AVHRR images

Walder¹,

MacLaren²

2000

International Journal of Remote Sensing

View full text Add to dashboard Cite

Arti cial neural networks trained on spectral and textural features extracted from Advanced Very High Resolution Radiometer (AVHRR) images have been used to develop an automated cloud classi cation system. Selection of the optimum combination of features was achieved by using statistical methods presented in earlier work by Gu et al. and by running large numbers of neural network simulations on test datasets. The performance of these methods surpasses that of other approaches such as the use of Gabor lters for texture segmentation and the maximum likelihood classi er. A particular architecture for an operational classi cation system is presented based on a two-stage multiple network con guration which is shown to segment complex images to a high degree of accuracy and achieves an overall accuracy on an independent, representative test set of 91%.

show abstract

Segmentation of Satellite Imagery Using Hierarchical Thresholding and Neural Networks

Cited by 51 publications

References 5 publications

Técnicas inteligentes para la identificación y el seguimiento de fenómenos meteorológicos que podrían afectar la seguridad de vuelo

Técnicas inteligentes para la identificación y el seguimiento de fenómenos meteorológicos que podrían afectar la seguridad de vuelo

A Dynamic Threshold Cloud Detecting Approach based on the Brightness Temperature from FY-2 VISSR Data

Neural network based methods for cloud classification on AVHRR images

Contact Info

Product

Resources

About