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Ship Detection and Recognition Using Hybrid Combination Algorithm.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Ship Detection and Recognition Using Hybrid Combination Algorithm./
作者:	Balasubramanian, Kaushalya Devi.
面頁冊數:	1 online resource (50 pages)
附註:	Source: Masters Abstracts International, Volume: 58-01.
Contained By:	Masters Abstracts International58-01(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780438199897

Ship Detection and Recognition Using Hybrid Combination Algorithm.
Balasubramanian, Kaushalya Devi.

Ship Detection and Recognition Using Hybrid Combination Algorithm. - 1 online resource (50 pages)

Source: Masters Abstracts International, Volume: 58-01.

Thesis (M.S.)--Texas A&M University - Kingsville, 2018.

Includes bibliographical references

Ship detection in the satellite images is gaining the attention of researchers as it has wide range of applications such as fishery management, maritime management, traffic monitoring etc. Considering the highly variable environment and weather conditions in closely spaced areas, obtaining a very controlled false alarm rate is the major problem for a non-homogenous sea clutter. since the SAR images are less influenced by weather conditions and time they are very suitable for ship detection. In a short period, the modern SARs can generate large amounts of data which brings the need for automatic detection of targets. These target detection systems carry out the detection process in three stages: identification, discrimination and classification. In this paper, we are proposing a new method of object detection by using the shape and color analysis, followed by morphological operation and GLCM. The proposed algorithm can be used to detect the crashed aero planes, floating containers and many other objects. The common CFAR has one false alarm while the results from the Hybrid combination algorithm matches with the ground truth.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438199897Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Ship Detection and Recognition Using Hybrid Combination Algorithm.
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100 1 $a Balasubramanian, Kaushalya Devi. $3 1193534
245 1 0 $a Ship Detection and Recognition Using Hybrid Combination Algorithm.
264 0 $c 2018
300 $a 1 online resource (50 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Masters Abstracts International, Volume: 58-01.
500 $a Adviser: Reza Nekovei.
502 $a Thesis (M.S.)--Texas A&M University - Kingsville, 2018.
504 $a Includes bibliographical references
520 $a Ship detection in the satellite images is gaining the attention of researchers as it has wide range of applications such as fishery management, maritime management, traffic monitoring etc. Considering the highly variable environment and weather conditions in closely spaced areas, obtaining a very controlled false alarm rate is the major problem for a non-homogenous sea clutter. since the SAR images are less influenced by weather conditions and time they are very suitable for ship detection. In a short period, the modern SARs can generate large amounts of data which brings the need for automatic detection of targets. These target detection systems carry out the detection process in three stages: identification, discrimination and classification. In this paper, we are proposing a new method of object detection by using the shape and color analysis, followed by morphological operation and GLCM. The proposed algorithm can be used to detect the crashed aero planes, floating containers and many other objects. The common CFAR has one false alarm while the results from the Hybrid combination algorithm matches with the ground truth.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Texas A&M University - Kingsville. $b Electrical Engineering and Computer Science. $3 1193535
773 0 $t Masters Abstracts International $g 58-01(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10830974 $z click for full text (PQDT)