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Deep Learning for Overhead Imagery: ...

Ortiz, Anthony.

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Deep Learning for Overhead Imagery: Algorithms and Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Learning for Overhead Imagery: Algorithms and Applications./
作者:	Ortiz, Anthony.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	115 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
Contained By:	Dissertations Abstracts International82-01B.
標題:	Artificial intelligence. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27997196
ISBN:	9798662388024

Deep Learning for Overhead Imagery: Algorithms and Applications.
Ortiz, Anthony.

Deep Learning for Overhead Imagery: Algorithms and Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 115 p.

Source: Dissertations Abstracts International, Volume: 82-01, Section: B.

Thesis (Ph.D.)--The University of Texas at El Paso, 2020.

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

Remote sensing using overhead imagery has critical impact to the way we understand our environment and offers crucial information for scene understanding, climate change research, disaster response, urban planning, forest management, and many other applications. At present, deep learning is increasingly used in remote sensing, but mostly borrowing algorithms developed for natural images in the computer vision community. Specific challenges arise while applying deep learning to remote sensing. These challenges include issues related to the high dimensionality and limited labeled data, security and robustness to adversarial attacks, and model generalization. In this thesis we focus on tackling these key challenges.We present an end-to-end framework to effectively integrate input feature subset selection into the training procedure of a deep neural network for dimensionality reduction. We show that our framework significantly improves performance on multispectral imageryapplications. We evaluate quantitatively the robustness of multispectral and hyperspectral image-based deep learning models to adversarial examples. Our experiments show that methods for generating adversarial examples designed for natural images are also effective for remote sensing imagery. We also introduce a framework that integrates dimensionality reduction, adversarial training, and a detector network that greatly improves models' robustness without sacrificing performance.We then present a novel network architecture which exploits conditional information to improve generalization of deep learning models. Finally, we propose a new normalization layer which facilitates transfer learning and improves performance across a great varietyof tasks. Local context normalization is a very efficient generalization of previous ones, it is invariant to batch size, and it is well-suited for transfer learning and interactive systems.This novel normalization layer provides state-of-the-art performance for the tasks of object detection, semantic segmentation, instance segmentation, and aerial image labeling.

ISBN: 9798662388024Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Deep learning

Deep Learning for Overhead Imagery: Algorithms and Applications.
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