Watershed image segmentation and cloud classification from multispectral MSG–SEVIRI imagery

González, Albano; Pérez, Juan Carlos; Muñoz, Jonathan; Méndez, Zebensui; Armas, Montserrat

doi:10.1016/j.asr.2011.09.023

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…Both the dilation and the erosion are basic operations in mathematical morphology and are part of conventional software packages such as IDL and MATLAB. Mathematically, the dilation of f ( x , y ) by a structuring element B , denoted as f ⊕ B , is equivalent to a local‐maximum operator [e.g., González et al ., ]:

((), f \oplus B) ((),, x, y) = max_{((), x^{'}, y') \in D ((), B)} ([], f ((), x - x^{'}, y - y')),

where ( x , y ) denotes the set of discrete spatial coordinates and D ( B ) is the domain of the structuring element B , i.e., the pixels from the neighborhood defined by B , where the maximum is evaluated. Similarly, the erosion of f ( x , y ) by a structuring element B , denoted as f ⊖ B , is equivalent to a local‐minimum operator:

((), f ⊖ B) ((),, x, y) = min_{((), x^{'}, {italicy}^{'}) \in D ((), B)} ([], f ((), x + x^{'}, y + y')) .

Combining equations and gives the morphological closing of f ( x , y ) by a structuring element B , denoted as C B ( f ):

C_{B} ((), f ((),, x, y)) = ((), f \oplus B) ⊖ B .

…”

Section: Methodsmentioning

confidence: 99%

“…Both the dilation and the erosion are basic operations in mathematical morphology and are part of conventional software packages such as IDL and MATLAB. Mathematically, the dilation of f(x,y) by a structuring element B, denoted as f ⊕ B, is equivalent to a local-maximum operator [e.g., González et al, 2012]:…”

Section: Watershed Segmentationmentioning

confidence: 99%

“…Although there are several applications of watershed segmentation in the literature [e.g., Bergès et al, 2009;González et al, 2012], to our knowledge, this is the first time it is used to detect organized MCC. We have also enhanced the technique to better identify the specific type of cloud structure of interest, the closed-cell marine stratocumulus.…”

Section: Introductionmentioning

confidence: 99%

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Impact of resolution on simulation of closed mesoscale cellular convection identified by dynamically guided watershed segmentation

et al. 2014

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Organized mesoscale cellular convection (MCC) is a common feature of marine stratocumulus clouds that forms in response to interactions among dynamic, microphysical, and radiative processes at the mesoscale. Cloud resolving models begin to resolve some of these processes, but using high resolutions is extremely costly. To understand the impact of limited resolution on MCC, we use the Weather Research and Forecasting model with chemistry and fully coupled cloud-aerosol interactions to simulate MCC over the southeast Pacific during the Variability of the American Monsoon Systems Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx). A suite of experiments with 3 and 9 km grid spacing indicates that the simulations with finer grid spacing have smaller liquid water paths and cloud fractions, while cloud tops are higher. The observed diurnal cycle is reasonably well simulated. To isolate organized MCC characteristics, we develop a new automated method that uses the watershed segmentation combining the detection of cloud boundaries with coincident vertical velocities. This ensures that the detected cloud fields are dynamically consistent for closed MCC, a common feature within the VOCALS-REx region. We demonstrate that the 3 km simulation is able to reproduce the scaling between horizontal cell size and boundary layer depth seen in satellite observations for the conditions of 900-1400 m deep boundary layers. However, the 9 km simulation is unable to resolve smaller circulations corresponding to shallower boundary layers, instead producing MCC with an invariant horizontal scale for all simulated boundary layers depths. The results demonstrate that the grid spacing needed for proper simulation of the MCC structure in marine stratocumulus regions depends on the boundary layer depth.

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((), f \oplus B) ((),, x, y) = max_{((), x^{'}, y') \in D ((), B)} ([], f ((), x - x^{'}, y - y')),

((), f ⊖ B) ((),, x, y) = min_{((), x^{'}, {italicy}^{'}) \in D ((), B)} ([], f ((), x + x^{'}, y + y')) .

Combining equations and gives the morphological closing of f ( x , y ) by a structuring element B , denoted as C B ( f ):

C_{B} ((), f ((),, x, y)) = ((), f \oplus B) ⊖ B .

…”

Section: Methodsmentioning

confidence: 99%

“…Both the dilation and the erosion are basic operations in mathematical morphology and are part of conventional software packages such as IDL and MATLAB. Mathematically, the dilation of f(x,y) by a structuring element B, denoted as f ⊕ B, is equivalent to a local-maximum operator [e.g., González et al, 2012]:…”

Section: Watershed Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of resolution on simulation of closed mesoscale cellular convection identified by dynamically guided watershed segmentation

et al. 2014

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“…Split-and-merge clustering allows to segment the scene in its natural groupings and label them as cloud, cloud-free land, uncertain. Gonzàlez et al in [23] performed cloud classification by using watershed image segmentation, this method has been tested on images from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infra-red Imager). The idea behind the method is to segment multispectral images using order-invariant watershed algorithms computed by a multi-dimensional morphological operator.…”

Section: Cloud Detectionmentioning

confidence: 99%

An Effective Satellite Remote Sensing Tool Combining Hardware and Software Solutions

Gugliuzza

Bruno

Ardizzone

et al. 2019

Communications in Computer and Information Science

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In this paper we propose a new effective remote sensing tool combining hardware and software solutions as an extension of our previous work. In greater detail the tool consists of a low cost receiver subsystem for public weather satellites and a signal and image processing module for several tasks such as signal and image enhancement, image reconstruction and cloud detection. Our solution allows to manage data from satellites effectively with low cost components and portable software solutions. We aim at sampling and processing of the modulated signal entirely in software enabled by Software Defined Radios (SDR) and CPU computational speed overcoming hardware limitation such as high receiver noise and low ADC resolution. Since we want to extend our previous method to demodulate signals coming from various meteorological satellites, we propose a new high frequency receiving system designed to receive and demodulate signals transmitted at 1.7 GHz. The signals coming from satellites are demodulated, synchronized and enhanced by using low level image processing techniques, then cloud detection is performed by using the well known K-means clustering algorithm. The hardware and software architecture extensions make our solution able to receive and demodulate high frequency and bandwidth meteorological satellite signals, such as those transmitted by NOAA POES, NOAA GOES, EU-METSAT Metop, Meteor-M and FengYun. Francesco Gugliuzza and Alessandro Bruno contributed equally to this work. The contribution of Alessandro Bruno falls within the activities of the current project titled "I telescopi Cherenkov per lo sviluppo tecnologico e culturale della Sicilia" at INAF-IASF Palermo, under the scientific supervision of Researcher Dr. Anna Anzalone.

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“…In [6], a normalized cuts model -that was first introduced by Jianbo Shi in [20] -is used to segment satellite cloud images: the image is represented by a weighted graph, where nodes represent pixels and edge weights represent a measure of similarity between pixels. In [21], authors implemented a segmentation method based on watershed algorithm: this later is applied to a gradient image obtained using a multi-dimensional morphological gradient, authors used a statistical approach to merge adjacent regions and solve the over-segmentation problem.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Multi-level Thresholding Method for Weather Satellite Cloud Segmentation

Idder¹,

Laachfoubi²

2015

IJCA

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Segmentation is one of the most important tasks in image processing, it seeks to determine whether an intensity value corresponds to a predefined class. Global thresholding is the simplest method for segmentation, it separates the image into two distinct classes corresponding to intensity values located below and above a threshold. However, global thresholding methods have a tendency to over-segment or under-segment areas with relatively inhomogeneous intensity. Multi-level thresholding takes into account spatial variations of intensity in an image, it is obtained by applying to each region of the image a different threshold. In this paper we present an unsupervised multi-level thresholding technique for segmenting cloud areas from weather satellites images. Our approach is to initially generate several binary images from a set of predefined threshold values, then extracting and mapping the contours of the cloudy areas included in the image sequence. The segmented image will comprise all regions whose contour coincides with the outline of a region in the original image. General Terms:Image analysis, Weather satellite imagery

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Watershed image segmentation and cloud classification from multispectral MSG–SEVIRI imagery

Cited by 8 publications

References 16 publications

Impact of resolution on simulation of closed mesoscale cellular convection identified by dynamically guided watershed segmentation

Impact of resolution on simulation of closed mesoscale cellular convection identified by dynamically guided watershed segmentation

An Effective Satellite Remote Sensing Tool Combining Hardware and Software Solutions

Unsupervised Multi-level Thresholding Method for Weather Satellite Cloud Segmentation

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