東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Feature extraction and classificatio...

Sharghi, Elan.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Feature extraction and classification of clouds in high resolution panchromatic satellite imagery./
Author:	Sharghi, Elan.
Description:	48 p.
Notes:	Source: Masters Abstracts International, Volume: 51-05.
Contained By:	Masters Abstracts International51-05(E).
Subject:	Engineering, Electronics and Electrical. -
Online resource:	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.
LDR:02256nam a2200289 4500 001 1958859
005 20140512081839.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303024009
035 $a (MiAaPQ)AAI1535732
035 $a AAI1535732
040 $a MiAaPQ $c MiAaPQ
100 1 $a Sharghi, Elan. $3 2094084
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.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Remote Sensing. $3 1018559
690 $a 0544
690 $a 0799
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).
790 $a 0033
791 $a M.S.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1535732