Supervised detection of bomb craters in historical aerial images using convolutional neural networks
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
In: Clermont, D.; Kruse, C.; Rottensteiner, F.; Heipke, C.: Supervised detection of bomb craters in historical aerial images using convolutional neural networks. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42 (2019), Nr. 2/W16, S. 67-74. DOI: https://doi.org/10.5194/isprs-archives-XLII-2-W16-67-2019
- Ereignis
-
Veröffentlichung
- (wo)
-
Hannover
- (wer)
-
Gottfried Wilhelm Leibniz Universität Hannover
- (wann)
-
2019
- Ereignis
-
Veröffentlichung
- (wo)
-
Hannover
- (wer)
-
Technische Informationsbibliothek (TIB)
- (wann)
-
2019
- Urheber
-
Clermont, D.
Kruse, C.
Rottensteiner, F.
Heipke, C.
- DOI
-
10.15488/10182
- URN
-
urn:nbn:de:101:1-2020111201242140539176
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:26 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Clermont, D.
- Kruse, C.
- Rottensteiner, F.
- Heipke, C.
- Gottfried Wilhelm Leibniz Universität Hannover
- Technische Informationsbibliothek (TIB)
Entstanden
- 2019
Ähnliche Objekte (12)
MARKED POINT PROCESSES FOR THE AUTOMATIC DETECTION OF BOMB CRATERS IN AERIAL WARTIME IMAGES
SUPERVISED DETECTION OF BOMB CRATERS IN HISTORICAL AERIAL IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
USING REDUNDANT INFORMATION FROM MULTIPLE AERIAL IMAGES FOR THE DETECTION OF BOMB CRATERS BASED ON MARKED POINT PROCESSES
Marked point processes for the automatic detection of bomb craters in aerial wartime images
TOWARDS BETTER CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS
CNN-BASED MULTI-SCALE HIERARCHICAL LAND USE CLASSIFICATION FOR THE VERIFICATION OF GEOSPATIAL DATABASES
CONFIDENCE-AWARE PEDESTRIAN TRACKING USING A STEREO CAMERA
INVESTIGATIONS ON SKIP-CONNECTIONS WITH AN ADDITIONAL COSINE SIMILARITY LOSS FOR LAND COVER CLASSIFICATION
PRECISE VEHICLE RECONSTRUCTION FOR AUTONOMOUS DRIVING APPLICATIONS
ASSIGNING TIE POINTS TO A GENERALISED BUILDING MODEL FOR UAS IMAGE ORIENTATION
CONTEXT MODELS FOR CRF-BASED CLASSIFICATION OF MULTITEMPORAL REMOTE SENSING DATA
RELATIVE POSE ESTIMATION USING IMAGE FEATURE TRIPLETS
MARKED POINT PROCESSES FOR THE AUTOMATIC DETECTION OF BOMB CRATERS IN AERIAL WARTIME IMAGES
SUPERVISED DETECTION OF BOMB CRATERS IN HISTORICAL AERIAL IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
USING REDUNDANT INFORMATION FROM MULTIPLE AERIAL IMAGES FOR THE DETECTION OF BOMB CRATERS BASED ON MARKED POINT PROCESSES
Marked point processes for the automatic detection of bomb craters in aerial wartime images
TOWARDS BETTER CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS
CNN-BASED MULTI-SCALE HIERARCHICAL LAND USE CLASSIFICATION FOR THE VERIFICATION OF GEOSPATIAL DATABASES
CONFIDENCE-AWARE PEDESTRIAN TRACKING USING A STEREO CAMERA
INVESTIGATIONS ON SKIP-CONNECTIONS WITH AN ADDITIONAL COSINE SIMILARITY LOSS FOR LAND COVER CLASSIFICATION
PRECISE VEHICLE RECONSTRUCTION FOR AUTONOMOUS DRIVING APPLICATIONS
ASSIGNING TIE POINTS TO A GENERALISED BUILDING MODEL FOR UAS IMAGE ORIENTATION
CONTEXT MODELS FOR CRF-BASED CLASSIFICATION OF MULTITEMPORAL REMOTE SENSING DATA
RELATIVE POSE ESTIMATION USING IMAGE FEATURE TRIPLETS
MARKED POINT PROCESSES FOR THE AUTOMATIC DETECTION OF BOMB CRATERS IN AERIAL WARTIME IMAGES
SUPERVISED DETECTION OF BOMB CRATERS IN HISTORICAL AERIAL IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
USING REDUNDANT INFORMATION FROM MULTIPLE AERIAL IMAGES FOR THE DETECTION OF BOMB CRATERS BASED ON MARKED POINT PROCESSES
Marked point processes for the automatic detection of bomb craters in aerial wartime images
TOWARDS BETTER CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS
CNN-BASED MULTI-SCALE HIERARCHICAL LAND USE CLASSIFICATION FOR THE VERIFICATION OF GEOSPATIAL DATABASES
CONFIDENCE-AWARE PEDESTRIAN TRACKING USING A STEREO CAMERA
INVESTIGATIONS ON SKIP-CONNECTIONS WITH AN ADDITIONAL COSINE SIMILARITY LOSS FOR LAND COVER CLASSIFICATION
PRECISE VEHICLE RECONSTRUCTION FOR AUTONOMOUS DRIVING APPLICATIONS
ASSIGNING TIE POINTS TO A GENERALISED BUILDING MODEL FOR UAS IMAGE ORIENTATION
CONTEXT MODELS FOR CRF-BASED CLASSIFICATION OF MULTITEMPORAL REMOTE SENSING DATA