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Hyperspectral remote sensing image a...

Bedagkar, Apurva V.

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Hyperspectral remote sensing image analysis for citrus pest monitoring.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hyperspectral remote sensing image analysis for citrus pest monitoring./
作者:	Bedagkar, Apurva V.
面頁冊數:	76 p.
附註:	Source: Masters Abstracts International, Volume: 44-01, page: 0459.
Contained By:	Masters Abstracts International44-01.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1427304
ISBN:	9780542181153

Hyperspectral remote sensing image analysis for citrus pest monitoring.
Bedagkar, Apurva V.

Hyperspectral remote sensing image analysis for citrus pest monitoring. - 76 p.

Source: Masters Abstracts International, Volume: 44-01, page: 0459.

Thesis (M.S.)--Texas A&M University - Kingsville, 2005.

The field of remote sensing has undergone tremendous development in the past decades. One of the fastest growing applications of remote sensing is precision agriculture. Perhaps no one area of remote sensing has had as many implications for the environmental and agricultural studies as hyperspectral imaging.

ISBN: 9780542181153Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Hyperspectral remote sensing image analysis for citrus pest monitoring.
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500 $a Chair: Reza Nekovei.
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520 $a The field of remote sensing has undergone tremendous development in the past decades. One of the fastest growing applications of remote sensing is precision agriculture. Perhaps no one area of remote sensing has had as many implications for the environmental and agricultural studies as hyperspectral imaging.
520 $a Hyperspectral imaging data provides high spatial and spectral resolution information which is used to estimate tree parameters that can be in turn used to detect pest stress in the trees. This information not only helps detect pest infestation, but it is also useful in extracting information about the type of infestation, and the extent of the same. This makes it possible to continuously monitor the health of trees and maximize profits, by maintaining good quality and high nutritional value. The choice of image analysis methods is critical for hyperspectral image analysis, because the method should be capable of taking proper advantage of the abundance information contained in the image. In this thesis, simultaneous individual tree crown delineation and treetop detection was chosen because the results obtained are suitable for further analysis in extraction of tree parameters for pest-monitoring.
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