Remote Sensing of Submerged Aquatic Vegetation

Cho, Hyun Jung; Mishra, Deepak R.; Wood, John R.

doi:10.5772/35156

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…The systematic and intelligent analyses of the remote sensing images provide a unique opportunity for understanding how, when, and where changes take place in our world. Precious information exploited from spatial repositories has been promoting benefits on many areas, such as agricultural [1,2] (forecast of harvests and soil erosion), hydric [3] (use of water resources and verification of the water quality), urban [4] (urban planning and demographic inferences), forest [5][6][7] (monitoring deforestation and biomass control), limnology [8] (characterization of aquatic vegetation and identification of water types), meteorology [9] (weather and climate studies), air traffic [10] (information for safety in the air), and national security [11] (military strategic planning of operations and missions).…”

Section: Introductionmentioning

confidence: 99%

Multiagent System for Image Mining

Freitas¹,

SantosSilva²

2017

Multi-Agent Systems

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The overdone growth, wide availability, and demands for remote sensing databases combined with human limits to analyze such huge datasets lead to a need to investigate tools, techniques, methodologies, and theories capable of assisting humans at extracting knowledge. Image mining arises as a solution to extract implicit knowledge intelligently and semiautomatically or other patterns not explicitly stored in the huge image databases. However, spatial databases are among the ones with the fastest growth due to the volume of spatial information produced many times a day, demanding the investigation of other means for knowledge extraction. Multiagent systems are composed of multiple computing elements known as agents that interact to pursuit their goals. Agents have been used to explore information in the distributed, open, large, and heterogeneous platforms. Agent mining is a potential technology that studies ways of interaction and integration between data mining and agents. This area brought advances to the technologies involved such as theories, methodologies, and solutions to solve relevant issues more precisely, accurately and faster. AgentGeo is evidence of this, a multiagent system of satellite image mining that, promotes advances in the state of the art of agent mining, since it relevant functions to extract knowledge from spatial databases.

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