A comprehensive review on deep learning based remote sensing image super-resolution methods
Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …
Earth Science field. However, due to the limitation of the optic and sensor technologies and …
Deep learning-based change detection in remote sensing images: A review
Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …
development of remote sensing (RS) technology. These images significantly enhance the …
[HTML][HTML] A review on deep learning in UAV remote sensing
Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …
capability, and brought important breakthroughs for processing images, time-series, natural …
Object detection and image segmentation with deep learning on earth observation data: A review-part i: Evolution and recent trends
Deep learning (DL) has great influence on large parts of science and increasingly
established itself as an adaptive method for new challenges in the field of Earth observation …
established itself as an adaptive method for new challenges in the field of Earth observation …
Object detection and image segmentation with deep learning on Earth observation data: A review—Part II: Applications
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …
investigating aggregated classes. The increase in data with a very high spatial resolution …
Fusatnet: Dual attention based spectrospatial multimodal fusion network for hyperspectral and lidar classification
With recent advances in sensing, multimodal data is becoming easily available for various
applications, especially in remote sensing (RS), where many data types like multispectral …
applications, especially in remote sensing (RS), where many data types like multispectral …
[图书][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …
learning in the field of earth sciences, from four leading voices Deep learning is a …
A generalizable and accessible approach to machine learning with global satellite imagery
Combining satellite imagery with machine learning (SIML) has the potential to address
global challenges by remotely estimating socioeconomic and environmental conditions in …
global challenges by remotely estimating socioeconomic and environmental conditions in …
Evaluation, tuning and interpretation of neural networks for working with images in meteorological applications
I Ebert-Uphoff, K Hilburn - Bulletin of the American …, 2020 - journals.ametsoc.org
Evaluation, Tuning, and Interpretation of Neural Networks for Working with Images in
Meteorological Applications in: Bulletin of the American Meteorological Society Volume 101 Issue …
Meteorological Applications in: Bulletin of the American Meteorological Society Volume 101 Issue …
U-Net convolutional networks for mining land cover classification based on high-resolution UAV imagery
Mining activities are the leading cause of deforestation, land-use changes, and pollution.
Land use/cover mapping in Vietnam every five years is not useful to monitor land covers in …
Land use/cover mapping in Vietnam every five years is not useful to monitor land covers in …