Automatic Classification of Remote Sensing Image Using Ant Colony Clustering Algorithm

Zhang, Hannv; Huang, Fang; Guo, Jiachen; Liu, Min

doi:10.1109/cisp.2009.5300869

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Although ants are blind, they can always be able to find a shortcut between food sources and their nest by indirect communication with each ant whiles it is walking. A special chemical trail (pheromone) is left on the ground during their trips [2]. This chemical trail guides the other ants towards the target solution by choosing a shortest path, which is called "convergence" in mathematical terms [8].…”

Section: Ant Colony Algorithm Conceptmentioning

confidence: 99%

“…One is the visual interpretation and the other is computer automatic classification. The computer automatic classification uses the pattern recognition technology and the artificial intelligence technology [2].…”

Section: Introductionmentioning

confidence: 99%

“…In 2009, Zhang, Huang, and Liu have used Ants to obtain the number of cluster results through intelligent searching the optimum solutions after limited iterations [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vegetation Loss Detection of Nineveh Province Using Remote Sensing Images Based on Ant Colony Algorithm

Abbosh¹,

Khaleel²,

Al-Khalidy³

2013

(AREJ)

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Nineveh province in Iraq has experienced a process of land cover conversion and vegetation loss especially in last decades. It is important to get accurate information on vegetation loss and changes in areas that are used for agriculture. Among the most effective methods to study and get information about this phenomenon is remote sensing technology.Since classical approaches lack of accuracy, artificial intelligence has been introduced to strengthen feature detection which leads to better classification. This paper uses ant colony algorithm to study and classify part of Nineveh province land into six classes. These are Agriculture land/flood plane, Water, Outcrop, Origin of early sand sheet, Desertable area, and Sand dunes. The variation in these six classes from 1987 to 2009 is shown. Results show that agriculture region and flood plain decreased from around 31% in 1987 to 11.2% of total area in 2009 while origin of early sand sheet and desertable area increased from 42.7% to around 49%. Beside that sand dune appears in 2009 to form about 26.47% of total area under study.

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Section: Ant Colony Algorithm Conceptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%