자료유형  

 학위논문

Control Number  

0016932830

International Standard Book Number  

9798379848750

Dewey Decimal Classification Number  

630

Main Entry-Personal Name  

Hasheminasab, Seyyed Meghdad.

Publication, Distribution, etc. (Imprint  

[S.l.] : Purdue University., 2022

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2022

Physical Description  

1 online resource(231 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-01, Section: A.

General Note  

Advisor: Habib, Ayman.

Dissertation Note  

Thesis (Ph.D.)--Purdue University, 2022.

Restrictions on Access Note  

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

Summary, Etc.  

요약Modern remote sensing platforms such as unmanned aerial vehicles (UAVs) that can carry a variety of sensors including RGB frame cameras, hyperspectral (HS) line cameras, and LiDAR sensors are commonly used in several application domains. In order to derive accurate products such as point clouds and orthophotos, sensors' interior and exterior orientation parameters (IOP and EOP) must be established. These parameters are derived/refined in a triangulation framework through minimizing the discrepancy between conjugate features extracted from involved datasets. Existing triangulation approaches are not general enough to deal with varying nature of data from different sensors/platforms acquired in diverse environmental conditions. This research develops a generic triangulation framework that can handle different types of primitives (e.g., point, linear, and/or planar features), and sensing modalities (e.g., RGB cameras, HS cameras, and/or LiDAR sensors) for delivering accurate products under challenging conditions with a primary focus on digital agriculture and stockpile monitoring application domains.The developed framework in this research starts with a fully-automated triangulation strategy that relies on available UAV trajectory information for reducing point feature matching ambiguity in RGB images acquired over agricultural fields. Then, a multi-scale matching strategy is introduced for automated triangulation of frame/line camera images acquired at different flying heights. To assure a good quality of generated orthophotos from UAV images captured at low altitudes over tall plants, plant row segments are extracted/matched in imagery and used as linear features in the triangulation process. Finally, a linear feature-based triangulation of image/LiDAR data captured by proximal sensing systems with challenging viewing geometry is introduced for indoor stockpile monitoring. Experimental results from real datasets demonstrate the feasibility of the proposed multi-primitive, multi-modal triangulation framework in providing accurate IOP/EOP, and consequently, accurate points clouds/orthophotos.

Subject Added Entry-Topical Term  

Agriculture.

Subject Added Entry-Topical Term  

Cameras.

Subject Added Entry-Topical Term  

Remote sensing.

Subject Added Entry-Topical Term  

Lasers.

Subject Added Entry-Topical Term  

Sensors.

Subject Added Entry-Topical Term  

Unmanned aerial vehicles.

Subject Added Entry-Topical Term  

Navigation systems.

Subject Added Entry-Topical Term  

Registration.

Subject Added Entry-Topical Term  

Geometry.

Subject Added Entry-Topical Term  

Aerospace engineering.

Subject Added Entry-Topical Term  

Optics.

Subject Added Entry-Topical Term  

Robotics.

Subject Added Entry-Topical Term  

Transportation.

Added Entry-Corporate Name  

Purdue University.

Host Item Entry  

Dissertations Abstracts International. 85-01A.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:643788