Deep learning and process understanding for data-driven Earth system science
Abstract Machine learning approaches are increasingly used to extract patterns and insights
from the ever-increasing stream of geospatial data, but current approaches may not be …
from the ever-increasing stream of geospatial data, but current approaches may not be …
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties–A review
Forthcoming superspectral satellite missions dedicated to land monitoring, as well as
planned imaging spectrometers, will unleash an unprecedented data stream. The …
planned imaging spectrometers, will unleash an unprecedented data stream. The …
A unified vegetation index for quantifying the terrestrial biosphere
Empirical vegetation indices derived from spectral reflectance data are widely used in
remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf …
remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf …
[PDF][PDF] Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks
We propose a deep Convolutional Neural Network (CNN) for land cover map** in remote
sensing images, with a focus on urban areas. In remote sensing, class imbalance represents …
sensing images, with a focus on urban areas. In remote sensing, class imbalance represents …
Deep supervised learning for hyperspectral data classification through convolutional neural networks
Spectral observations along the spectrum in many narrow spectral bands through
hyperspectral imaging provides valuable information towards material and object …
hyperspectral imaging provides valuable information towards material and object …
Unsupervised deep feature extraction for remote sensing image classification
This paper introduces the use of single-layer and deep convolutional networks for remote
sensing data analysis. Direct application to multi-and hyperspectral imagery of supervised …
sensing data analysis. Direct application to multi-and hyperspectral imagery of supervised …
Hyperspectral remote sensing data analysis and future challenges
Hyperspectral remote sensing technology has advanced significantly in the past two
decades. Current sensors onboard airborne and spaceborne platforms cover large areas of …
decades. Current sensors onboard airborne and spaceborne platforms cover large areas of …
Advances in spectral-spatial classification of hyperspectral images
Recent advances in spectral-spatial classification of hyperspectral images are presented in
this paper. Several techniques are investigated for combining both spatial and spectral …
this paper. Several techniques are investigated for combining both spatial and spectral …
Advances in hyperspectral image classification: Earth monitoring with statistical learning methods
The technological evolution of optical sensors over the last few decades has provided
remote sensing analysts with rich spatial, spectral, and temporal information. In particular …
remote sensing analysts with rich spatial, spectral, and temporal information. In particular …
Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete
Support vector machines (SVMs) have recently been used to model the properties of low
volume fly ash self-compacting concrete (LVF-SCC) by means of kernel functions to …
volume fly ash self-compacting concrete (LVF-SCC) by means of kernel functions to …