Determination of spatial and temporal characteristics as an aid to neural network cloud classification

Lewis, Hugh G.; Côté, Stéphane; Tatnall, A.R.L.

doi:10.1080/014311697218827

Cited by 22 publications

(18 citation statements)

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“…The SOLI value for a convex polygon with no holes is 1.0, and the value for a concave polygon is less than 1.0. ELONG is the ratio between the length and height of the region bounding rectangle enclosing the minimum area (Sonka et al, 1993;Lewis et al, 1997). The ELONG value for a square is 1.0, and the value for a polygon whose minimum enclosing rectangle is not a square is greater than 1.0.…”

Section: Metrics To Characterize the Shape Of Land Use Segmentsmentioning

confidence: 99%

“…Quantitatively characterizing the land use classes in imagery will foundamentally support the land use classification and LUCC analysis. There have been many studies that have focused on utilizing the spatial information in remotely sensed imagery for land use and land-cover classification (Lewis et al, 1997;Thakur and Dikshit, 1997;Narumalani et al 1998;Ji 2000;Steele 2000;Steele and Redmond, 2001;Herold et al, 2002). Spatial information in remote sensing imagery includes aspects such as image texture, contextual information, and geometric attributes of features (Narumalani et al 1998;Pacifici et al, 2009).…”

Section: Instructionsmentioning

confidence: 99%

“…There are also various metrics to describe a single shape characteristic. For example, researchers employed compact index (Russ, 2002), complexity index (Lewis et al, 1997), SqP (Frohn, 2006), and fractal dimension (Herold et al, 2002;Read and Lam, 2002) to characterize the shape complexity of a patch. Which indices are effective to characterize the land use patches of different classes?…”

Section: Instructionsmentioning

confidence: 99%

“…Zhou et al (1995) employed a shape index to help to discriminate spectrally similar land-cover classes. Lewis et al (1997) used shape descriptors such as eccentricity, elongatedness and complexity in cloud classification from satellite imagery. Narumalani et al (1998) focused on incorporating the geometric attributes of image objects to resolve some of the spectral confusions that occur when pure per-pixel classification algorithms are used.…”

Section: Instructionsmentioning

confidence: 99%

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Analyzing the Shape Characteristics of Land Use Classes in Remote Sensing Imagery

Jiao

Liu

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Shape is an important aspect of spatial attributes of land use segments in remotely sensed imagery, but it is still rarely used as a component in land use classification or image-based land use analysis. This study aims to quantitatively characterize land use classes using shape metrics. The study is conducted in a case area located in south China, covering twelve scenes of SPOT-5 images. There were total ten metrics selected for the analysis, namely, Convexity (CONV), Solidity (SOLI), Elongation (ELONG), Roundness (ROUND), Rectangular Fitting (RECT), Compact (COMP), Form Factor (FORM), Square pixel metric (SqP), Fractal Dimension (FD), and Shape Index (SI). The last five metrics were used to measure the complexity of shape. Eight land use classes were investigated in the case area, namely, roads, cultivated lands, settlement places, rivers, ponds, forest and grass lands, reservoirs, and dams. The results show that all typical shape properties of land use segments can be well measured by shape metrics. We identified the land use classes whose values are significantly differentiated from most classes for each metric. Two of the five complexity metrics, FORM and SqP, were identified to be more effective in characterizing the complexity of land use classes. We finally selected six shape metrics and deduced the "Shape Metric Signatures" (SMS) of different land use classes. SMS can serve as accurate and predictive discriminators of land use classes within the study area. Our results show that SMS can clearly distinguish spectrally similar land use classes. The results will help to build a more accurate and intelligent object-oriented classification system for land use classes.

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Section: Metrics To Characterize the Shape Of Land Use Segmentsmentioning

confidence: 99%

Section: Instructionsmentioning

confidence: 99%

Section: Instructionsmentioning

confidence: 99%

Section: Instructionsmentioning

confidence: 99%

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Analyzing the Shape Characteristics of Land Use Classes in Remote Sensing Imagery

Jiao

Liu

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) has been employed for cloud detection (Lewis et al 1997). They used it for the detection of clouds in Meteosat images using the visible channel (0.4-1.1µm).…”

Section: Introductionmentioning

confidence: 99%

A Very Quick Neural Network Algorithm for Cloud Detection

Al-Rawi¹,

Casanova²,

Vasileisky³

2003

Geocarto International

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Satellite based remote sensing of weather and climate: recent achievements and future perspectives

Thies

Bendix

2011

Meteorological Applications

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Spaceborne remote sensing provides valuable information about the state of the Earth-atmosphere system and its components in an area-wide and continuous manner. Over the past 50 years a range of satellite platforms carrying many different sensors has been constructed to monitor atmospheric parameters used in meteorological and climatological studies, and the information retrieved from satellite-based sensors has greatly enhanced our understanding of the processes and dynamics within the Earth-atmosphere system. The present paper gives an overview of existing satellites and sensors, together with the developed algorithms to retrieve meteorological and climatological parameters. Furthermore, it gives an outlook on new systems planned for the near future.

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Determination of spatial and temporal characteristics as an aid to neural network cloud classification

Cited by 22 publications

References 0 publications

Analyzing the Shape Characteristics of Land Use Classes in Remote Sensing Imagery

Analyzing the Shape Characteristics of Land Use Classes in Remote Sensing Imagery

A Very Quick Neural Network Algorithm for Cloud Detection

Satellite based remote sensing of weather and climate: recent achievements and future perspectives

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