Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Salberg, Arnt Børre"'
Multi-modality data is becoming readily available in remote sensing (RS) and can provide complementary information about the Earth's surface. Effective fusion of multi-modal information is thus important for various applications in RS, but also very
Externí odkaz:
http://arxiv.org/abs/2111.03845
Capturing global contextual representations by exploiting long-range pixel-pixel dependencies has shown to improve semantic segmentation performance. However, how to do this efficiently is an open question as current approaches of utilising attention
Externí odkaz:
http://arxiv.org/abs/2009.01599
We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation. Building on the recently proposed Self-Constructing Graph (SCG) module, which makes use of learnable latent var
Externí odkaz:
http://arxiv.org/abs/2004.10327
Graph Neural Networks (GNNs) have received increasing attention in many fields. However, due to the lack of prior graphs, their use for semantic labeling has been limited. Here, we propose a novel architecture called the Self-Constructing Graph (SCG)
Externí odkaz:
http://arxiv.org/abs/2003.06932
Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the semantic segmentation task. In
Externí odkaz:
http://arxiv.org/abs/2003.04027
It is important, but challenging, for the forest industry to accurately map roads which are used for timber transport by trucks. In this work, we propose a Dense Dilated Convolutions Merging Network (DDCM-Net) to detect these roads in lidar images. T
Externí odkaz:
http://arxiv.org/abs/1909.04588
We propose a network for semantic mapping called the Dense Dilated Convolutions Merging Network (DDCM-Net) to provide a deep learning approach that can recognize multi-scale and complex shaped objects with similar color and textures, such as building
Externí odkaz:
http://arxiv.org/abs/1908.11799
Autor:
Kampffmeyer, Michael, Løkse, Sigurd, Bianchi, Filippo M., Livi, Lorenzo, Salberg, Arnt-Børre, Jenssen, Robert
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of
Externí odkaz:
http://arxiv.org/abs/1902.04981
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation July 2022 111
