Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Mahdyar Ravanbakhsh"'
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-13
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. To address MLC problems, the use of deep neural networks that require a high number of reliable
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on convolutional neural networks (CNNs) have shown strong performance gai
Autor:
Tom-Lukas Breitkopf, Leonard Hackel, Mahdyar Ravanbakhsh, Anne-Karin Cooke, Sandra Willkommen, Stefan Broda, Begüm Demir
Subsurface tile drainage pipes provide agronomic, economic and environmental benefits. By lowering the water table of wet soils, they improve the aeration of plant roots and ultimately increase the productivity of farmland. They do however also provi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::580e6a7e0233e0d6c2276b4d510ab519
http://arxiv.org/abs/2210.02071
http://arxiv.org/abs/2210.02071
Publikováno v:
IEEE International Geoscience and Remote Sensing Symposium
IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium
IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium
The rapid evolution of satellite imaging systems has resulted in sharp increases of image archive volumes. Multitemporal images constitute a sizeable portion of these time-series databases. Accordingly, development of accurate content based time-seri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76911aaba1c60f1148901477d841100b
Autor:
Lucio Marcenaro, Damian Campo, Mahdyar Ravanbakhsh, Mohamad Baydoun, Carlo S. Regazzoni, Pablo Marin, David Martin
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 22:3372-3386
The technology for autonomous vehicles is close to replacing human drivers by artificial systems endowed with high-level decision-making capabilities. In this regard, systems must learn about the usual vehicle's behavior to predict imminent difficult
Publikováno v:
2022 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS).
The development of cross-modal retrieval systems that can search and retrieve semantically relevant data across different modalities based on a query in any modality has attracted great attention in remote sensing (RS). In this paper, we focus our at
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c228c60e1fdd5958ea860d84d43194b4
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this problem, publicly available themat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d22e99134685a95435e4b28789d68e2d
Publikováno v:
IGARSS
Deep Neural Networks have recently demonstrated promising performance in binary change detection (CD) problems in remote sensing (RS), requiring a large amount of labeled multitemporal training samples. Since collecting such data is time-consuming an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::362c4f07f054d1514f502f7129140460
http://arxiv.org/abs/2007.02565
http://arxiv.org/abs/2007.02565
Autor:
Mahdyar Ravanbakhsh, Vadim Tschernezki, Felix Last, Tassilo Klein, Kayhan Batmanghelich, Volker Tresp, Moin Nabi
Publikováno v:
ICASSP
Proc IEEE Int Conf Acoust Speech Signal Process
Proc IEEE Int Conf Acoust Speech Signal Process
Image segmentation is a ubiquitous step in almost any medical image study. Deep learning-based approaches achieve state-of-the-art in the majority of image segmentation benchmarks. However, end-to-end training of such models requires sufficient annot