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 …
Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven
Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
[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 …
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
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 …
[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 …
Review of pixel-level remote sensing image fusion based on deep learning
Z Wang, Y Ma, Y Zhang - Information Fusion, 2023 - Elsevier
The booming development of remote sensing images in many visual tasks has led to an
increasing demand for obtaining images with more precise details. However, it is impractical …
increasing demand for obtaining images with more precise details. However, it is impractical …
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 …
A comprehensive survey on imputation of missing data in internet of things
The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …
communication technologies, and Internet protocols with broad applications. Collecting data …
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 …