An automated change detection system

Sloggett, D. R.; Gurney, C.; Newton, W. H.; Gooding, Renee; Dowman, I. J.

doi:10.1109/igarss.1994.399288

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…These values are often standardized to avoid the ambiguity resulting from identical difference values created from different original pixel values. This approach is fast, simple, and requires minimal user intervention (Sloggett et al, 1994). Image ratioing divides one image's pixel value by the other.…”

Section: Data Processing and Analysis For Image Differencing Methodsmentioning

confidence: 99%

“…There is a substantial body of literature addressing many aspects of change detection using satellite-based remote sensing data (Jensen, 1981;Riordan, 1981;Lund, 1983;Park et al, 1983;Milne, 1988;Caselles and Garcia, 1989;Coppin and Bauer, 1994;Mattikalli, 1994;Ngai and Curlander, 1994;Sloggett et al, 1994;Franklin et al, 1995;Hogda et al, 1995;Solheim et al, 1995;Hashem et al, 1996;Mahlke, 1996;Parra et al, 1996;Lyon et al, 1998;Brewer et al, 2000;Coppin et al, 2001). Lu et al (2003) and Coppin et al (2004) provide detailed reviews and discussions of change detection methods.…”

Section: Introductionmentioning

confidence: 99%

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Classifying and Mapping Wildfire Severity

Brewer¹,

Winne²,

Redmond³

et al. 2005

photogramm eng remote sensing

157

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This study evaluates six different approaches to classifying and mapping fire severity using multi-temporal Landsat Thematic Mapper data. The six approaches tested include: two based on temporal image differencing and ratioing between pre-fire and post-fire images, two based on principal component analysis of pre-and post-fire imagery, and two based on artificial neural networks, one using just postfire imagery and the other both pre-and post-fire imagery. Our results demonstrated the potential value for any of these methods to provide quantitative fire severity maps, but one of the image differencing methods (ND4/7) provided a flexible, robust, and analytically simple approach that could be applied anywhere in the Continental U.S.Based on the results of this test, the ND4/7 was implemented operationally to classify and map fire severity over

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Section: Data Processing and Analysis For Image Differencing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classifying and Mapping Wildfire Severity

Brewer¹,

Winne²,

Redmond³

et al. 2005

photogramm eng remote sensing

157

View full text Add to dashboard Cite

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An assessment of natural and human-induced changes along Hurghada and Ras Abu Soma coastal area, Red Sea, Egypt

El-Gamily¹,

Nasr²,

El-Raey³

2001

Int. J. Remote Sensing

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The Red Sea coastal zone is characterized by its sensitive, fragile, unique natural resources and habitats. In the Hurghada coastal region, major changes in the tourism industry have taken place in the last few decades. The detection of environmental changes, in a selected site of the Red Sea coastal zone, will be helpful to protect and develop this coastal environment.A methodology for separating natural and man-made changes in satellite images was developed. It was based on the following assumptions: (1) slow changes, which occur within the range of the class re ectance, represent a natural change rather than an anthropogenic one; (2) natural changes tend to be in the same land-use/land-cover class in each date, i.e. slow changes in the re ectance, not leading to changes in the type of land-use/land-cover class from the master image to the destination one; and (3) rapid changes in the re ectance of the Earth's objects are usually related to anthropogenic activities. This technique is used to identify and assess changes along the coast of Hurghada and Ras Abu Soma, the Red Sea.Results indicate serious human impacts and the necessity for control measures and monitoring. Recommendations are presented.

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Requirements and techniques for an automated change detection system

Dai

Khorram

1998

IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings.

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An automated change detection system

Cited by 3 publications

References 2 publications

Classifying and Mapping Wildfire Severity

Classifying and Mapping Wildfire Severity

An assessment of natural and human-induced changes along Hurghada and Ras Abu Soma coastal area, Red Sea, Egypt

Requirements and techniques for an automated change detection system

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