190 research outputs found
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
Learning Aligned Cross-Modal Representations from Weakly Aligned Data
People can recognize scenes across many different modalities beyond natural
images. In this paper, we investigate how to learn cross-modal scene
representations that transfer across modalities. To study this problem, we
introduce a new cross-modal scene dataset. While convolutional neural networks
can categorize cross-modal scenes well, they also learn an intermediate
representation not aligned across modalities, which is undesirable for
cross-modal transfer applications. We present methods to regularize cross-modal
convolutional neural networks so that they have a shared representation that is
agnostic of the modality. Our experiments suggest that our scene representation
can help transfer representations across modalities for retrieval. Moreover,
our visualizations suggest that units emerge in the shared representation that
tend to activate on consistent concepts independently of the modality.Comment: Conference paper at CVPR 201
Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports
Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports
Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review
This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives-for application-based opportunities, with emphasis on those that address big data with geospatial components
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Fast embedding for image classification & retrieval and its application to the hostel industry
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContent-based image classification and retrieval are the automatic processes of taking
an unseen image input and extracting its features representing the input image. Then,
for the classification task, this mathematically measured input is categorized according
to established criteria in the server and consequently shows the output as a result. On
the other hand, for the retrieval task, the extracted features of an unseen query image
are sent to the server to search for the most visually similar images to a given image
and retrieve these images as a result. Despite image features could be represented
by classical features, artificial intelligence-based features, Convolutional Neural
Networks (CNN) to be precise, have become powerful tools in the field. Nonetheless,
the high dimensional CNN features have been a challenge in particular for applications
on mobile or Internet of Things devices. Therefore, in this thesis, several fast
embeddings are explored and proposed to overcome the constraints of low memory,
bandwidth, and power. Furthermore, the first hostel image database is created with
three datasets, hostel image dataset containing 13,908 interior and exterior images of
hostels across the world, and Hostels-900 dataset and Hostels-2K dataset containing
972 images and 2,380 images, respectively, of 20 London hostel buildings. The results
demonstrate that the proposed fast embeddings such as the application of GHM-Rand
operator, GHM-Fix operator, and binary feature vectors are able to outperform or give
competitive results to those state-of-the-art methods with a lot less computational
resource. Additionally, the findings from a ten-year literature review of CBIR study in
the tourism industry could picturize the relevant research activities in the past decade
which are not only beneficial to the hostel industry or tourism sector but also to the
computer science and engineering research communities for the potential real-life
applications of the existing and developing technologies in the field
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Identifying and interpreting fetal standard scan planes during 2D ultrasound
mid-pregnancy examinations are highly complex tasks which require years of
training. Apart from guiding the probe to the correct location, it can be
equally difficult for a non-expert to identify relevant structures within the
image. Automatic image processing can provide tools to help experienced as well
as inexperienced operators with these tasks. In this paper, we propose a novel
method based on convolutional neural networks which can automatically detect 13
fetal standard views in freehand 2D ultrasound data as well as provide a
localisation of the fetal structures via a bounding box. An important
contribution is that the network learns to localise the target anatomy using
weak supervision based on image-level labels only. The network architecture is
designed to operate in real-time while providing optimal output for the
localisation task. We present results for real-time annotation, retrospective
frame retrieval from saved videos, and localisation on a very large and
challenging dataset consisting of images and video recordings of full clinical
anomaly screenings. We found that the proposed method achieved an average
F1-score of 0.798 in a realistic classification experiment modelling real-time
detection, and obtained a 90.09% accuracy for retrospective frame retrieval.
Moreover, an accuracy of 77.8% was achieved on the localisation task.Comment: 12 pages, 8 figures, published in IEEE Transactions in Medical
Imagin
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