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@ARTICLE{Bazi:911346,
author = {Bazi, Yakoub and Cavallaro, Gabriele and Demir, Begüm and
Melgani, Farid},
title = {{L}earning from {D}ata for {R}emote {S}ensing {I}mage
{A}nalysis},
journal = {International journal of remote sensing},
volume = {43},
number = {15-16},
issn = {0143-1161},
address = {London},
publisher = {Taylor $\&$ Francis},
reportid = {FZJ-2022-04638},
pages = {5527 - 5533},
year = {2022},
abstract = {Recent advances in satellite technology have led to a
regular, frequent and high- resolution monitoring of Earth
at the global scale, providing an unprecedented amount of
Earth observation (EO) data. The growing operational
capability of global Earth monitoring from space provides a
wealth of information on the state of our planet Earth that
waits to be mined for several different EO applications,
e.g. climate change analysis, urban area studies, forestry
applications, risk and damage assessment, water quality
assessment, crop monitoring and so on. Recent studies in
machine learning have triggered substantial performance gain
for the above-mentioned tasks. Advanced machine learning
models such as deep convolutional neural networks (CNNs),
recursive neural networks and transformers have recently
made great progress in a wide range of remote sensing (RS)
tasks, such as object detection, RS image classification,
image captioning and so on. The study of Bai et al. (2021)
analyzes the research progress, hotspots, trends and methods
in the field of deep learning in remote sensing, and deep
learning is becoming an important tool for remote sensing
and has been widely used in numerous remote sensing tasks
related to image processing and analysis. In this context,
the present special issue aims at gathering a collection of
papers in the most advanced and trendy areas dealing with
learning from data and with applications to remote sensing
image analysis. The manuscripts can be subdivided into five
groups depending mainly on the processing or learning task.
A specific collection for hyperspectral imagery has been
included given the special attention by the remote sensing
community to this kind of data.},
cin = {JSC},
ddc = {620},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511)},
pid = {G:(DE-HGF)POF4-5111},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000882850800001},
doi = {10.1080/01431161.2022.2131481},
url = {https://juser.fz-juelich.de/record/911346},
}