134 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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
Sensin
Background-Mixed Augmentation for Weakly Supervised Change Detection
Change detection (CD) is to decouple object changes (i.e., object missing or
appearing) from background changes (i.e., environment variations) like light
and season variations in two images captured in the same scene over a long time
span, presenting critical applications in disaster management, urban
development, etc. In particular, the endless patterns of background changes
require detectors to have a high generalization against unseen environment
variations, making this task significantly challenging. Recent deep
learning-based methods develop novel network architectures or optimization
strategies with paired-training examples, which do not handle the
generalization issue explicitly and require huge manual pixel-level annotation
efforts. In this work, for the first attempt in the CD community, we study the
generalization issue of CD from the perspective of data augmentation and
develop a novel weakly supervised training algorithm that only needs
image-level labels. Different from general augmentation techniques for
classification, we propose the background-mixed augmentation that is
specifically designed for change detection by augmenting examples under the
guidance of a set of background-changing images and letting deep CD models see
diverse environment variations. Moreover, we propose the augmented & real data
consistency loss that encourages the generalization increase significantly. Our
method as a general framework can enhance a wide range of existing deep
learning-based detectors. We conduct extensive experiments in two public
datasets and enhance four state-of-the-art methods, demonstrating the
advantages of our method. We release the code at
https://github.com/tsingqguo/bgmix.Comment: AAAI 2023 Accepte
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