Deep learning in environmental remote sensing: Achievements and challenges
Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …
environmental remote sensing research. With an increasing amount of “big data” from earth …
[HTML][HTML] High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques
Normalized difference vegetation index (NDVI) derived from satellites has been ubiquitously
utilized in the field of remote sensing. Nevertheless, there are multitudinous contaminations …
utilized in the field of remote sensing. Nevertheless, there are multitudinous contaminations …
[HTML][HTML] Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion
Optical remote sensing imagery is at the core of many Earth observation activities. The
regular, consistent and global-scale nature of the satellite data is exploited in many …
regular, consistent and global-scale nature of the satellite data is exploited in many …
[HTML][HTML] Temporal convolutional neural network for the classification of satellite image time series
Latest remote sensing sensors are capable of acquiring high spatial and spectral Satellite
Image Time Series (SITS) of the world. These image series are a key component of …
Image Time Series (SITS) of the world. These image series are a key component of …
[HTML][HTML] How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
AY Sun, BR Scanlon - Environmental Research Letters, 2019 - iopscience.iop.org
Big Data and machine learning (ML) technologies have the potential to impact many facets
of environment and water management (EWM). Big Data are information assets …
of environment and water management (EWM). Big Data are information assets …
Hyperspectral image denoising employing a spatial–spectral deep residual convolutional neural network
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the
performance of the subsequent HSI interpretation and applications. In this paper, a novel …
performance of the subsequent HSI interpretation and applications. In this paper, a novel …
Cooperated spectral low-rankness prior and deep spatial prior for HSI unsupervised denoising
Model-driven methods and data-driven methods have been widely developed for
hyperspectral image (HSI) denoising. However, there are pros and cons in both model …
hyperspectral image (HSI) denoising. However, there are pros and cons in both model …
Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors
Cloud detection is an important preprocessing step for the precise application of optical
satellite imagery. In this paper, we propose a deep learning based cloud detection method …
satellite imagery. In this paper, we propose a deep learning based cloud detection method …
[HTML][HTML] Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020-iMap World 1.0
Longer time high-resolution, high-frequency, consistent, and more detailed land cover data
are urgently needed in order to achieve sustainable development goals on food security …
are urgently needed in order to achieve sustainable development goals on food security …
Mixed noise removal in hyperspectral image via low-fibered-rank regularization
The tensor tubal rank, defined based on the tensor singular value decomposition (t-SVD),
has obtained promising results in hyperspectral image (HSI) denoising. However, the …
has obtained promising results in hyperspectral image (HSI) denoising. However, the …