國立虎尾科技大學 |

Methods to Improve the Predictive Performance of Deep Learning Systems for Aerial Roof Building Detection for Multi-Spectral Images.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Methods to Improve the Predictive Performance of Deep Learning Systems for Aerial Roof Building Detection for Multi-Spectral Images./
Author:	Churi, Advait.
Description:	1 online resource (70 pages)
Notes:	Source: Masters Abstracts International, Volume: 82-08.
Contained By:	Masters Abstracts International82-08.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798569981007

Methods to Improve the Predictive Performance of Deep Learning Systems for Aerial Roof Building Detection for Multi-Spectral Images.
Churi, Advait.

Methods to Improve the Predictive Performance of Deep Learning Systems for Aerial Roof Building Detection for Multi-Spectral Images. - 1 online resource (70 pages)

Source: Masters Abstracts International, Volume: 82-08.

Thesis (M.S.)--University of Massachusetts Lowell, 2021.

Includes bibliographical references

In this thesis work, we study the challenges for multispectral visible-infrared aerial images. We present a dilated convolution neural network-based deep learning system. The proposed system utilizes an automatic multispectral band selection strategy and automatic histogram-based threshold finding for building roof segmentation to improve the deep learning system prediction accuracy. Current existing systems train one model on all the data at once. These systems usually use RGB (Red, Green, Blue) bands and avoid relevant post-processing of the predicted images. We present a novel method to solve some of these object detection and recognition challenges. In particular, we 1) implement separate models for each dataset, 2) propose an optimal automatic band selection strategy to choose the most informative bands from given multispectral images, 3) present a histogram-based automatic optimal threshold finding technique for segmentation. Our results show that having a separate model for each city rather than only one model for all the combined cities increases the prediction accuracy from by 72.54% to 87.61% (by 20.77%) for RGB (Red, Green, Blue) bands and from 82.99% to 83.87% (by 1.06%) for bands selected via the proposed band selection strategy. As our experimental results show, our proposed deep learning system has a 35.6% increase in building detection prediction accuracy F1-score(from 0.688% to 0.933%) compared to the winning implementation of SpaceNet Building Detection US National challenge-2 (2017) for the city of Rio.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798569981007Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Predictive performanceIndex Terms--Genre/Form:

554714
Electronic books.

Methods to Improve the Predictive Performance of Deep Learning Systems for Aerial Roof Building Detection for Multi-Spectral Images.
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520 $a In this thesis work, we study the challenges for multispectral visible-infrared aerial images. We present a dilated convolution neural network-based deep learning system. The proposed system utilizes an automatic multispectral band selection strategy and automatic histogram-based threshold finding for building roof segmentation to improve the deep learning system prediction accuracy. Current existing systems train one model on all the data at once. These systems usually use RGB (Red, Green, Blue) bands and avoid relevant post-processing of the predicted images. We present a novel method to solve some of these object detection and recognition challenges. In particular, we 1) implement separate models for each dataset, 2) propose an optimal automatic band selection strategy to choose the most informative bands from given multispectral images, 3) present a histogram-based automatic optimal threshold finding technique for segmentation. Our results show that having a separate model for each city rather than only one model for all the combined cities increases the prediction accuracy from by 72.54% to 87.61% (by 20.77%) for RGB (Red, Green, Blue) bands and from 82.99% to 83.87% (by 1.06%) for bands selected via the proposed band selection strategy. As our experimental results show, our proposed deep learning system has a 35.6% increase in building detection prediction accuracy F1-score(from 0.688% to 0.933%) compared to the winning implementation of SpaceNet Building Detection US National challenge-2 (2017) for the city of Rio.
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