Back to Search
Start Over
Très haute résolution multi-temporelle depuis l'espace : résultats du challenge de fusion de données de l'IEEE GRSS 2016
- Source :
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2017, 10 (8), pp.3435-3447. ⟨10.1109/JSTARS.2017.2696823⟩
- Publication Year :
- 2017
- Publisher :
- HAL CCSD, 2017.
-
Abstract
- International audience; In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 images and a video captured by the Iris camera on-board the International Space Station. The problems addressed and the techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and mixed ideas and methodologies from the remote sensing, video processing, and computer vision. In particular, the winning team developed a deep learning method to jointly address spatial scene labeling and temporal activity modeling using the available image and video data. The second place team proposed a random field model to simultaneously perform coregistration of multitemporal data, semantic segmentation, and change detection. The methodological key ideas of both these approaches and the main results of the corresponding experimental validation are discussed in this paper.
- Subjects :
- FUSION DONNEES
CONVOLUTIONAL NEURAL NETWORKS (CNN)
RESEAU NEURONES
DEEP LEARNING
ANALYSE IMAGE
[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
VIDEO FROM SPACE
IMAGE ANALYSIS AND DATA FUSION
MULTISOURCE
DETECTION CHANGEMENT
TRACKING
CHANGE DETECTION
RANDOM FIELDS
MULTIRESOLUTION
MULTIMODAL
Subjects
Details
- Language :
- English
- ISSN :
- 19391404
- Database :
- OpenAIRE
- Journal :
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2017, 10 (8), pp.3435-3447. ⟨10.1109/JSTARS.2017.2696823⟩
- Accession number :
- edsair.dedup.wf.001..db2c41d03b9dc5fd880d8c5ee829693a