東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Comparison of Transformer-Based and ...

Belcaid, Mareyam,

FindBook

Google Book

Amazon

博客來

Comparison of Transformer-Based and Convolutional Neural Network-Based (CNN) Models for Remote Sensing Image Classification /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Comparison of Transformer-Based and Convolutional Neural Network-Based (CNN) Models for Remote Sensing Image Classification // Mareyam Belcaid.
作者:	Belcaid, Mareyam,
面頁冊數:	1 electronic resource (60 pages)
附註:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
標題:	Codes. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31011653
ISBN:	9798382562544

Comparison of Transformer-Based and Convolutional Neural Network-Based (CNN) Models for Remote Sensing Image Classification /
Belcaid, Mareyam,

Comparison of Transformer-Based and Convolutional Neural Network-Based (CNN) Models for Remote Sensing Image Classification /Mareyam Belcaid. - 1 electronic resource (60 pages)

Source: Masters Abstracts International, Volume: 85-11.

Remote sensing is regarded as a rich source of data that is still very valuable for mapping (classification) and monitoring of this information using various methodologies. Convolutional Neural Networks (CNNs) are frequently employed by researchers in this field as one of the key feature extraction approaches in application to satellite pictures and have achieved good performance and efficacy. Yet, they face some problems, including the overfitting problem and the need for large datasets and expensive computational resources. As a result, it would be a good idea to experiment with various Deep Learning methods, investigate them, and compare them to existing methods while taking into account all of the elements that can affect the processing. Transformers, particularly vision Transformers, have just been offered as novel Deep Learning approaches and have demonstrated good performance in a variety of domains. Hence, experimenting with these new models would be a realistic strategy in terms of learning how they behave when processing data and what value they can contribute to the picture classification areaIn relation with this, the present work aims to analyze the performance of different DL methods in classifying different land cover/land use types depending on their properties and the effect of the revolution level on this.Experimental results conducted on two main datasets "EuroSAT" and "UC Merced" indicates that some CNNs, such as, "ResNET50" and "EfficientNET B0" perform well with different resolutions while for "VGG16" and the Vision Transformer, the need for a huge amount of data for the learning task is unavoidable.

English

ISBN: 9798382562544Subjects--Topical Terms:

3560019
Codes.

Comparison of Transformer-Based and Convolutional Neural Network-Based (CNN) Models for Remote Sensing Image Classification /
LDR:03072nmm a22003973i 4500 001 2437665
005 20251124054954.5
006 m o d
007 cr|nu||||||||
008 261011s2023 miu||||||m |||||||eng d
020 $a 9798382562544
035 $a (MiAaPQD)AAI31011653
035 $a (MiAaPQD)Portugal10362150959
035 $a AAI31011653
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Belcaid, Mareyam, $e author. $3 3828731
245 1 0 $a Comparison of Transformer-Based and Convolutional Neural Network-Based (CNN) Models for Remote Sensing Image Classification / $c Mareyam Belcaid.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (60 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-11.
500 $a Advisors: Meyer, Hanna; sospedra, Joaquin Torres; Knoth, Christian.
502 $b M.S. $c Universidade NOVA de Lisboa (Portugal) $d 2023.
520 $a Remote sensing is regarded as a rich source of data that is still very valuable for mapping (classification) and monitoring of this information using various methodologies. Convolutional Neural Networks (CNNs) are frequently employed by researchers in this field as one of the key feature extraction approaches in application to satellite pictures and have achieved good performance and efficacy. Yet, they face some problems, including the overfitting problem and the need for large datasets and expensive computational resources. As a result, it would be a good idea to experiment with various Deep Learning methods, investigate them, and compare them to existing methods while taking into account all of the elements that can affect the processing. Transformers, particularly vision Transformers, have just been offered as novel Deep Learning approaches and have demonstrated good performance in a variety of domains. Hence, experimenting with these new models would be a realistic strategy in terms of learning how they behave when processing data and what value they can contribute to the picture classification areaIn relation with this, the present work aims to analyze the performance of different DL methods in classifying different land cover/land use types depending on their properties and the effect of the revolution level on this.Experimental results conducted on two main datasets "EuroSAT" and "UC Merced" indicates that some CNNs, such as, "ResNET50" and "EfficientNET B0" perform well with different resolutions while for "VGG16" and the Vision Transformer, the need for a huge amount of data for the learning task is unavoidable.
546 $a English
590 $a School code: 7029
650 4 $a Codes. $3 3560019
650 4 $a Neural networks. $3 677449
650 4 $a Remote sensing. $3 535394
690 $a 0800
690 $a 0799
710 2 $a Universidade NOVA de Lisboa (Portugal). $e degree granting institution. $3 3767537
720 1 $a Meyer, Hanna $e degree supervisor.
720 1 $a sospedra, Joaquin Torres $e degree supervisor.
720 1 $a Knoth, Christian $e degree supervisor.
773 0 $t Masters Abstracts International $g 85-11.
790 $a 7029
791 $a M.S.
792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31011653