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

Single-Image Dehazing on Aerial Imagery Using Convoultional Neural Networks

Madadikhaljan, Mojgan and Bahmanyar, Reza and Azimi, Seyedmajid and Reinartz, Peter and Sörgel, Uwe (2019) Single-Image Dehazing on Aerial Imagery Using Convoultional Neural Networks. In: ISPRS International GeoSpatial Conference, pp. 1-6. ISPRS. ISPRS International GeoSpatial Conference, 2019-10-12 - 2019-10-14, Tehran, Iran. doi: 10.5194/isprs-archives-XLII-4-W18-687-2019.

[img] PDF


Haze contains floating particles in the air which can result in image quality degradation and visibility reduction in airborne data. Haze removal task has several applications in image enhancement and can improve the performance of automatic image analysis systems, namely object detection and segmentation. Unlike rich haze removal literature in ground imagery, there is a lack of methods specifically designed for aerial imagery, considering the fact that there is a characteristic difference between the aerial imagery domain and ground one. In this paper, we propose a method to dehaze aerial images using Convolutional Neural Networks~(CNNs). Currently, there is no available data for dehazing methods in aerial imagery. To address this issue, we have created a synthetically-hazed aerial image dataset to train the neural network on aerial hazy image dataset. We train All-in-One dehazing network (AOD-Net) as the base approach on hazy aerial images and compare the performance of our proposed approach against the classical model. We have tested our model on natural as well as the synthetically-hazed aerial images. Both qualitative and quantitative results of the adapted network show an improvement in dehazing results. We show that the adapted AOD-Net on our aerial image test set increases PSNR and SSim by 2.2% and 9%, respectively.

Item URL in elib:https://elib.dlr.de/128952/
Document Type:Conference or Workshop Item (Speech)
Title:Single-Image Dehazing on Aerial Imagery Using Convoultional Neural Networks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Bahmanyar, RezaUNSPECIFIEDhttps://orcid.org/0000-0002-6999-714X139400245
Azimi, SeyedmajidUNSPECIFIEDhttps://orcid.org/0000-0002-6084-2272UNSPECIFIED
Reinartz, PeterUNSPECIFIEDhttps://orcid.org/0000-0002-8122-1475UNSPECIFIED
Journal or Publication Title:ISPRS International GeoSpatial Conference
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-6
Keywords:Single-image Dehazing, Convolutional Neural Networks, Aerial Imagery, Haze Removal, Hazy Image Generation
Event Title:ISPRS International GeoSpatial Conference
Event Location:Tehran, Iran
Event Type:international Conference
Event Start Date:12 October 2019
Event End Date:14 October 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - NGC KoFiF (old), V - D.MoVe (old), V - UrMo Digital (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Bahmanyar, Gholamreza
Deposited On:04 Sep 2019 13:20
Last Modified:24 Apr 2024 20:32

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.