297 research outputs found
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 data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
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UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation
Change detection (CD) by comparing two bi-temporal images is a crucial task
in remote sensing. With the advantages of requiring no cumbersome labeled
change information, unsupervised CD has attracted extensive attention in the
community. However, existing unsupervised CD approaches rarely consider the
seasonal and style differences incurred by the illumination and atmospheric
conditions in multi-temporal images. To this end, we propose a change detection
with domain shift setting for remote sensing images. Furthermore, we present a
novel unsupervised CD method using a light-weight transformer, called
UCDFormer. Specifically, a transformer-driven image translation composed of a
light-weight transformer and a domain-specific affinity weight is first
proposed to mitigate domain shift between two images with real-time efficiency.
After image translation, we can generate the difference map between the
translated before-event image and the original after-event image. Then, a novel
reliable pixel extraction module is proposed to select significantly
changed/unchanged pixel positions by fusing the pseudo change maps of fuzzy
c-means clustering and adaptive threshold. Finally, a binary change map is
obtained based on these selected pixel pairs and a binary classifier.
Experimental results on different unsupervised CD tasks with seasonal and style
changes demonstrate the effectiveness of the proposed UCDFormer. For example,
compared with several other related methods, UCDFormer improves performance on
the Kappa coefficient by more than 12\%. In addition, UCDFormer achieves
excellent performance for earthquake-induced landslide detection when
considering large-scale applications. The code is available at
\url{https://github.com/zhu-xlab/UCDFormer}Comment: 16 pages, 7 figures, IEEE Transactions on Geoscience and Remote
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