DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Optimization of Convolutional Neural Networks for Hedgerow Object Detection in Very High-Resolution Satellite Images

Ahlswede, Steve (2020) Optimization of Convolutional Neural Networks for Hedgerow Object Detection in Very High-Resolution Satellite Images. Master's, Universität Trier.

Full text not available from this repository.


Hedgerows are one of the few remaining natural landscape features within agricultural areas. They provide important ecosystem services, living space for numerous species, and mitigate habitat fragmentation by acting as corridors between habitat patches. Environmental agencies in many European countries are thus interesting in monitoring and protecting hedgerows. In order to facilitate the success of such monitoring programs, cost-effective and accurate mapping of hedgerows is required. Here, the use of neural networks is examined in order to determine their feasibility for hedgerow mapping. Two state of the art neural networks were chosen (Mask R-CNN and DeepLab v3+). Both networks were able to successfully detect hedgerows, with DeepLab v3+ outperforming Mask R-CNN. Images from two different seasons (October and May) were tested as inputs. Finding suggest that using all available images, regardless of season, is preferred. Data augmentations were found to greatly increase performance for both networks, with DeepLab v3+ achieving up to 81% recall and 69% precision. However, caution should be taken when applying augmentations which modify spectral values of pixels. A pre-trained network was compared to a network with randomly initialized weights using DeepLab v3+. Here, the pre-trained network was found to produce superior recall and precision. However, the mask boundaries were far more precise using the randomly initialized network. The main limitation to successfully using randomly initialized weights was the amount of data available. Improvements can be made to Mask R-CNN by using rotatable anchors, as the network mainly struggled to detect diagonal hedgerows. Both neural networks were able to perform detections across a large spatial scale, and compared to previous object-based hedgerow mapping techniques, were relatively simple to use. Thus, this study supports the use of neural networks use in regional monitoring strategies for hedgerows

Item URL in elib:https://elib.dlr.de/133890/
Document Type:Thesis (Master's)
Title:Optimization of Convolutional Neural Networks for Hedgerow Object Detection in Very High-Resolution Satellite Images
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ahlswede, Stevesteve.ahlswede (at) dlr.deUNSPECIFIED
Date:January 2020
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Number of Pages:101
Keywords:Hedgerows, convolutional neural networks, deep learning, segmentation, very high-resolution, data augmentation
Institution:Universität Trier
Department:Environmental Science – Remote Sensing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Asam, Dr. Sarah
Deposited On:27 Jan 2020 13:03
Last Modified:27 Jan 2020 13:03

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.