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Deep Vision in Optical Imagery: From Perception to Reasoning

Mou, LiChao (2020) Deep Vision in Optical Imagery: From Perception to Reasoning. Dissertation, TU München.

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Official URL: https://mediatum.ub.tum.de/doc/1524790/1524790.pdf

Abstract

Deep learning has achieved extraordinary success in a wide range of tasks in computer vision field over the past years. Remote sensing data present different properties as compared to natural images/videos, due to their unique imaging technique, shooting angle, etc. For instance, hyperspectral images usually have hundreds of spectral bands, offering additional information, and the size of objects (e.g., vehicles) in remote sensing images is quite limited, which brings challenges for detection or segmentation tasks. This thesis focuses on two kinds of remote sensing data, namely hyper/multi-spectral and high-resolution images, and explores several methods to try to find answers to the following questions: - In comparison with natural images or videos in computer vision, the unique asset of hyper/multi-spectral data is their rich spectral information. But what this “additional” information brings for learning a network? And how do we take full advantage of these spectral bands? - Remote sensing images at high resolution have pretty different characteristics, bringing challenges for several tasks, for example, small object segmentation. Can we devise tailored networks for such tasks? - Deep networks have produced stunning results in a variety of perception tasks, e.g., image classification, object detection, and semantic segmentation. While the capacity to reason about relations over space is vital for intelligent species. Can a network/module with the capacity of reasoning benefit to parsing remote sensing data? To this end, a couple of networks are devised to figure out what a network learns from hyperspectral images and how to efficiently use spectral bands. In addition, a multi-task learning network is investigated for the instance segmentation of vehicles from aerial images and videos. Finally, relational reasoning modules are designed to improve semantic segmentation of aerial images.

Item URL in elib:https://elib.dlr.de/185907/
Document Type:Thesis (Dissertation)
Title:Deep Vision in Optical Imagery: From Perception to Reasoning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Date:2020
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:176
Status:Published
Keywords:deep learning models, optical remote sensing, hyperspectral, image classification, change detection, object detection, semantic segmentation, aerial images, ROSIS, AVIRIS
Institution:TU München
Department:Ingenieurfakultät Bau Geo Umwelt
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence, R - Optical remote sensing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Haschberger, Dr.-Ing. Peter
Deposited On:28 Mar 2022 13:20
Last Modified:28 Mar 2022 13:20

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