Cloud classification using the textural features of Meteosat images

Ameur, Zohra; Ameur, Soltane; Adane, A.; Sauvageot, Henri; Bara, K.

doi:10.1080/01431160410001735120

Cited by 38 publications

(24 citation statements)

References 21 publications

(23 reference statements)

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“…Therefore, a preliminary cloud detection was mandatory before the mosaicking operation. Automatic cloud screening methodologies have been developed mainly for lowresolution satellites images, such as AVHRR or VEGETATION, to produce temporal series of continental mosaics (Derrien et al 1993, Ameur et al 2004, Amato et al 2008). Very few studies have been carried out to provide automated cloud screening for high-or very high-resolution images in a tropical environment.…”

Section: Image Processingmentioning

confidence: 99%

Land use monitoring by remote sensing in tropical forest areas in support of the Kyoto Protocol: the case of French Guiana

Stach

Salvado

Petit

et al. 2009

International Journal of Remote Sensing

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The new SPOT/Envisat direct receiving station (DRS) operating in Cayenne in the framework of the SEAS-Guyane project was used to produce a global cloudless SPOT mosaic over French Guiana for the year 2006. This mosaic was used to perform a land-use, land-use change and forestry (LULUCF) inventory in the framework of the Kyoto Protocol. Nearly 17 000 sample points were laid down on the SPOT mosaic with a stratified sampling design. The land use at each sample point was determined by visual interpretation of the corresponding SPOT image in 2006 and the Landsat Thematic Mapper (TM) image from the Global Land Cover Facility (GLCF) in 1990. Statistics for the period 1990-2006 were computed and integrated in the first voluntary Kyoto inventory.

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Section: Image Processingmentioning

confidence: 99%

Land use monitoring by remote sensing in tropical forest areas in support of the Kyoto Protocol: the case of French Guiana

Stach

Salvado

Petit

et al. 2009

International Journal of Remote Sensing

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“…Finally, IR 10.8 is essential for measuring temperatures at sea and land surface and the top of clouds, and detection of cirrus clouds (INOUE, 1987). Another important application, used in the visualization section 4, is cloud structure classification which consists in an estimation of the cloud structure altitude using this multichannel information, yielding to a segmentation of the pixels into different types of clouds (SZANTAI and DÉSALMAND, 2005;AMEUR et al, 2004), as shown in Figure 2.…”

Section: Satellite Imagesmentioning

confidence: 99%

Multi-Channel Satellite Image Analysis Using a Variational Approach

Álvarez

Castaño

García

et al. 2008

Pure appl. geophys.

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Currently, meteorological satellites provide multichannel image sequences including visible, temperature and water vapor channels. Based on a variational approach, we propose mathematical models to address some of the usual challenges in satellite image analysis such as: (i) the estimation and smoothing of the cloud structures by decoupling them into different layers depending on their altitudes, (ii) the estimation of the cloud structure motion by combining information from all the channels, and (iii) the 3D visualization of both the cloud structure and the estimated displacements. We include information of all the channels in a single variational motion estimation model. The associated Euler-Lagrange equations yield to a nonlinear system of partial differential equations that we solve numerically using finite-difference schemes. We illustrate the performance of the proposed models with numerical experiments on two multichannel satellite sequences of the North Atlantic, one of them from the Hurricane Vince. Based on a realistic synthetic ground truth motion, we show that our multichannel approach overcomes the single channel estimation for both the average Euclidean and angular errors.

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“…Another technique that was used in the cloud classification field is based on textural features analysis which consists in distinguishing clouds by the spatial distribution characteristics of gray levels corresponding to a region in one specific channel (Ameur et al, 2004). While the spectral features of clouds may change, their textural properties are often distinct and tend to be less sensitive to the effects of atmospheric attenuation or detector noise.…”

Section: Introductionmentioning

confidence: 99%