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

切換: 標籤 | MARC模式 | ISBD

Semi-Supervised Semantic Segmentatio...

Vaidya, Ashlesha.

FindBook

Google Book

Amazon

博客來

Semi-Supervised Semantic Segmentation in UAV Imagery.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Semi-Supervised Semantic Segmentation in UAV Imagery./
作者:	Vaidya, Ashlesha.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	67 p.
附註:	Source: Masters Abstracts International, Volume: 82-04.
Contained By:	Masters Abstracts International82-04.
標題:	Artificial intelligence. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28087097
ISBN:	9798678127846

Semi-Supervised Semantic Segmentation in UAV Imagery.
Vaidya, Ashlesha.

Semi-Supervised Semantic Segmentation in UAV Imagery. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 67 p.

Source: Masters Abstracts International, Volume: 82-04.

Thesis (M.Sc.C.S.)--University of California, San Diego, 2020.

This item must not be sold to any third party vendors.

Mangrove forests are rich ecosystems that support our planet and the mankind in many unique ways. Unfortunately, these mangroves are declining at a rapid rate due to deforestation and other activities of the mankind. Monitoring and tracking of these mangrove trees is essential for their conservation. Machine Learning can be used for this purpose but to take advantage of the power of machine learning, image data needs to be captured for these mangrove ecosystems. This data collection is done using Unmanned Aerial Vehicles like drones in this research. Manually labeling the acquired image data for machine learning applications is a tedious and time-consuming task. This called for the development of architectures which could learn from limited labeled data and take advantage of the large amounts of unlabelled data. Such architectures are the semi-supervised semantic segmentation architectures and are studied in this research. We have shown how different semi-supervised models like the self-learning based Pseudo-Labelling architecture, the graph-based label propagation architecture and the deep leaning UNet-Autoencoder architecture perform on the task of mangrove segmentation in the aerial imagery. In order to evaluate different models we mainly look at the Intersection over Union because of it's popularity in segmentation tasks. Overall, we see that the deep learning UNet-Autoencoder architecture performs the best with an average IoU of 0.78. Conceivably, the performance of each of the models improves as more labelled data is provided for training. The highest IoU obtained in this research is 0.9 with the UNet-Autoencoder when as much as 75% of the data provided is labelled.

ISBN: 9798678127846Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Autoencoder

Semi-Supervised Semantic Segmentation in UAV Imagery.
LDR:02861nmm a2200385 4500 001 2279598
005 20210823080254.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798678127846
035 $a (MiAaPQ)AAI28087097
035 $a AAI28087097
040 $a MiAaPQ $c MiAaPQ
100 1 $a Vaidya, Ashlesha. $3 3558059
245 1 0 $a Semi-Supervised Semantic Segmentation in UAV Imagery.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 67 p.
500 $a Source: Masters Abstracts International, Volume: 82-04.
500 $a Advisor: Kastner, Ryan.
502 $a Thesis (M.Sc.C.S.)--University of California, San Diego, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Mangrove forests are rich ecosystems that support our planet and the mankind in many unique ways. Unfortunately, these mangroves are declining at a rapid rate due to deforestation and other activities of the mankind. Monitoring and tracking of these mangrove trees is essential for their conservation. Machine Learning can be used for this purpose but to take advantage of the power of machine learning, image data needs to be captured for these mangrove ecosystems. This data collection is done using Unmanned Aerial Vehicles like drones in this research. Manually labeling the acquired image data for machine learning applications is a tedious and time-consuming task. This called for the development of architectures which could learn from limited labeled data and take advantage of the large amounts of unlabelled data. Such architectures are the semi-supervised semantic segmentation architectures and are studied in this research. We have shown how different semi-supervised models like the self-learning based Pseudo-Labelling architecture, the graph-based label propagation architecture and the deep leaning UNet-Autoencoder architecture perform on the task of mangrove segmentation in the aerial imagery. In order to evaluate different models we mainly look at the Intersection over Union because of it's popularity in segmentation tasks. Overall, we see that the deep learning UNet-Autoencoder architecture performs the best with an average IoU of 0.78. Conceivably, the performance of each of the models improves as more labelled data is provided for training. The highest IoU obtained in this research is 0.9 with the UNet-Autoencoder when as much as 75% of the data provided is labelled.
590 $a School code: 0033.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Environmental studies. $3 2122803
650 4 $a Computer science. $3 523869
653 $a Autoencoder
653 $a Label propagation
653 $a Self-learning
653 $a Semantic segmentation
653 $a Semi-supervised learning
653 $a UNet
690 $a 0800
690 $a 0477
690 $a 0984
710 2 $a University of California, San Diego. $b Computer Science and Engineering. $3 1018473
773 0 $t Masters Abstracts International $g 82-04.
790 $a 0033
791 $a M.Sc.C.S.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28087097