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[HTML][HTML] Deep learning in remote sensing applications: A meta-analysis and review
Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …
image analysis over the past few years. In this study, the major DL concepts pertinent to …
[HTML][HTML] Survey of deep-learning approaches for remote sensing observation enhancement
Deep Learning, and Deep Neural Networks in particular, have established themselves as
the new norm in signal and data processing, achieving state-of-the-art performance in …
the new norm in signal and data processing, achieving state-of-the-art performance in …
Deep learning meets SAR: Concepts, models, pitfalls, and perspectives
Deep learning in remote sensing has received considerable international hype, but it is
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
[HTML][HTML] Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks
Unprecedented urbanization in particular in countries of the global south result in informal
urban development processes, especially in mega cities. With an estimated 1 billion slum …
urban development processes, especially in mega cities. With an estimated 1 billion slum …
Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery
Change detection is one of the central problems in earth observation and was extensively
investigated over recent decades. In this paper, we propose a novel recurrent convolutional …
investigated over recent decades. In this paper, we propose a novel recurrent convolutional …
A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection
In the task of change detection (CD), high-resolution remote sensing images (HRSIs) can
provide rich ground object information. However, the interference from noise and complex …
provide rich ground object information. However, the interference from noise and complex …
Backdoor pre-trained models can transfer to all
Pre-trained general-purpose language models have been a dominating component in
enabling real-world natural language processing (NLP) applications. However, a pre-trained …
enabling real-world natural language processing (NLP) applications. However, a pre-trained …
[HTML][HTML] Building instance classification using street view images
Land-use classification based on spaceborne or aerial remote sensing images has been
extensively studied over the past decades. Such classification is usually a patch-wise or …
extensively studied over the past decades. Such classification is usually a patch-wise or …
[HTML][HTML] Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series
Assessing land-use following deforestation is vital for reducing emissions from deforestation
and forest degradation. In this paper, for the first time, we assess the potential of spatial …
and forest degradation. In this paper, for the first time, we assess the potential of spatial …
[HTML][HTML] A deep learning framework for matching of SAR and optical imagery
SAR and optical imagery provide highly complementary information about observed scenes.
A combined use of these two modalities is thus desirable in many data fusion scenarios …
A combined use of these two modalities is thus desirable in many data fusion scenarios …