Zobrazeno 1 - 10
of 1 356
pro vyhledávání: '"Atkinson, P. M."'
Autor:
Ghamisi, Pedram, Yu, Weikang, Marinoni, Andrea, Gevaert, Caroline M., Persello, Claudio, Selvakumaran, Sivasakthy, Girotto, Manuela, Horton, Benjamin P., Rufin, Philippe, Hostert, Patrick, Pacifici, Fabio, Atkinson, Peter M.
The convergence of artificial intelligence (AI) and Earth observation (EO) technologies has brought geoscience and remote sensing into an era of unparalleled capabilities. AI's transformative impact on data analysis, particularly derived from EO plat
Externí odkaz:
http://arxiv.org/abs/2405.20868
Autor:
Chacón-Montalván, Erick A., Atkinson, Peter M., Nemeth, Christopher, Taylor, Benjamin M., Moraga, Paula
Spatial data are often derived from multiple sources (e.g. satellites, in-situ sensors, survey samples) with different supports, but associated with the same properties of a spatial phenomenon of interest. It is common for predictors to also be measu
Externí odkaz:
http://arxiv.org/abs/2403.08514
Convolutional Neural Networks (CNNs) are models that are utilized extensively for the hierarchical extraction of features. Vision transformers (ViTs), through the use of a self-attention mechanism, have recently achieved superior modeling of global c
Externí odkaz:
http://arxiv.org/abs/2308.05235
Publikováno v:
IEEE Geoscience and Remote Sensing Magazine, Volume 11, Issue 2, Pages 60-85, 2023
Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. T
Externí odkaz:
http://arxiv.org/abs/2212.09360
Autor:
Wang, Libo, Li, Rui, Zhang, Ce, Fang, Shenghui, Duan, Chenxi, Meng, Xiaoliang, Atkinson, Peter M.
Publikováno v:
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},volume = {190},pages = {196-214},year = {2022},issn = {0924-2716}
Semantic segmentation of remotely sensed urban scene images is required in a wide range of practical applications, such as land cover mapping, urban change detection, environmental protection, and economic assessment.Driven by rapid developments in d
Externí odkaz:
http://arxiv.org/abs/2109.08937
Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis. Along with rapid development in sensor technologies, remotely sensed images can be captured at multiple spatial resolutio
Externí odkaz:
http://arxiv.org/abs/2103.07935
Investment in measuring a process more completely or accurately is only useful if these improvements can be utilised during modelling and inference. We consider how improvements to data quality over time can be incorporated when selecting a modelling
Externí odkaz:
http://arxiv.org/abs/2102.00884
Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning. First, a multi
Externí odkaz:
http://arxiv.org/abs/2011.11005
Semantic segmentation of remote sensing images plays an important role in a wide range of applications including land resource management, biosphere monitoring and urban planning. Although the accuracy of semantic segmentation in remote sensing image
Externí odkaz:
http://arxiv.org/abs/2009.02130
Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder architecture, has been used frequently for image segmentation with high accu
Externí odkaz:
http://arxiv.org/abs/2007.13083