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Feature extraction and classificatio...

Sharghi, Elan.

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Feature extraction and classification of clouds in high resolution panchromatic satellite imagery.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Feature extraction and classification of clouds in high resolution panchromatic satellite imagery./
作者:	Sharghi, Elan.
面頁冊數:	48 p.
附註:	Source: Masters Abstracts International, Volume: 51-05.
Contained By:	Masters Abstracts International51-05(E).
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1535732
ISBN:	9781303024009

Feature extraction and classification of clouds in high resolution panchromatic satellite imagery.
Sharghi, Elan.

Feature extraction and classification of clouds in high resolution panchromatic satellite imagery. - 48 p.

Source: Masters Abstracts International, Volume: 51-05.

Thesis (M.S.)--University of California, San Diego, 2013.

The development of sophisticated remote sensing sensors is rapidly increasing, and the vast amount of satellite imagery collected is too much to be analyzed manually by a human image analyst. It has become necessary for a tool to be developed to automate the job of an image analyst. This tool would need to intelligently detect and classify objects of interest through computer vision algorithms. Existing software called the Rapid Image Exploitation Resource (RAPIERRTM) was designed by engineers at Space and Naval Warfare Systems Center Pacific (SSC PAC) to perform exactly this function. This software automatically searches for anomalies in the ocean and reports the detections as a possible ship object. However, if the image contains a high percentage of cloud coverage, a high number of false positives are triggered by the clouds.

ISBN: 9781303024009Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Feature extraction and classification of clouds in high resolution panchromatic satellite imagery.
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245 1 0 $a Feature extraction and classification of clouds in high resolution panchromatic satellite imagery.
300 $a 48 p.
500 $a Source: Masters Abstracts International, Volume: 51-05.
500 $a Adviser: Truong Q. Nguyen.
502 $a Thesis (M.S.)--University of California, San Diego, 2013.
520 $a The development of sophisticated remote sensing sensors is rapidly increasing, and the vast amount of satellite imagery collected is too much to be analyzed manually by a human image analyst. It has become necessary for a tool to be developed to automate the job of an image analyst. This tool would need to intelligently detect and classify objects of interest through computer vision algorithms. Existing software called the Rapid Image Exploitation Resource (RAPIERRTM) was designed by engineers at Space and Naval Warfare Systems Center Pacific (SSC PAC) to perform exactly this function. This software automatically searches for anomalies in the ocean and reports the detections as a possible ship object. However, if the image contains a high percentage of cloud coverage, a high number of false positives are triggered by the clouds.
520 $a The focus of this thesis is to explore various feature extraction and classification methods to accurately distinguish clouds from ship objects. An examination of a texture analysis method, line detection using the Hough transform, and edge detection using wavelets are explored as possible feature extraction methods. The features are then supplied to a K-Nearest Neighbors (KNN) or Support Vector Machine (SVM) classifier. Parameter options for these classifiers are explored and the optimal parameters are determined.
590 $a School code: 0033.
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650 4 $a Remote Sensing. $3 1018559
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710 2 $a University of California, San Diego. $b Electrical Engineering (Signal and Image Proc). $3 1020523
773 0 $t Masters Abstracts International $g 51-05(E).
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