10,127 research outputs found
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
Change detection (CD) is an important application of remote sensing, which
provides timely change information about large-scale Earth surface. With the
emergence of hyperspectral imagery, CD technology has been greatly promoted, as
hyperspectral data with the highspectral resolution are capable of detecting
finer changes than using the traditional multispectral imagery. Nevertheless,
the high dimension of hyperspectral data makes it difficult to implement
traditional CD algorithms. Besides, endmember abundance information at subpixel
level is often not fully utilized. In order to better handle high dimension
problem and explore abundance information, this paper presents a General
End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image
change detection (HSI-CD). The main contributions of this work are threefold:
1) Mixed-affinity matrix that integrates subpixel representation is introduced
to mine more cross-channel gradient features and fuse multi-source information;
2) 2-D CNN is designed to learn the discriminative features effectively from
multi-source data at a higher level and enhance the generalization ability of
the proposed CD algorithm; 3) A new HSI-CD data set is designed for the
objective comparison of different methods. Experimental results on real
hyperspectral data sets demonstrate the proposed method outperforms most of the
state-of-the-arts
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
Speaker change detection using BIC: a comparison on two datasets
Abstract — This paper addresses the problem of unsupervised speaker change detection. We assume that there is no prior knowledge on the number of speakers or their identities. Two methods are tested. The first method uses the Bayesian Information Criterion (BIC), investigates the AudioSpectrumCentroid and AudioWaveformEnvelope features, and implements a dynamic thresholding followed by a fusion scheme. The second method is a real-time one that uses a metric-based approach employing line spectral pairs (LSP) and the BIC criterion to validate a potential change point. The experiments are carried out on two different datasets. The first set was created by concatenating speakers from the TIMIT database and is referred to as the TIMIT data set. The second set was created by using recordings from the MPEG-7 test set CD1 and broadcast news and is referred to as the INESC dataset. I
Automatic speaker segmentation using multiple features and distance measures: a comparison of three approaches
This paper addresses the problem of unsupervised speaker change detection. Three systems based on the Bayesian Information Criterion (BIC) are tested. The first system investigates the AudioSpectrumCentroid and the AudioWaveformEnvelope features, implements a dynamic thresholding followed by a fusion scheme, and finally applies BIC. The second method is a real-time one that uses a metric-based approach employing the line spectral pairs and the BIC to validate a potential speaker change point. The third method consists of three modules. In the first module, a measure based on second-order statistics is used; in the second module, the Euclidean distance and T2 Hotelling statistic are applied; and in the third module, the BIC is utilized. The experiments are carried out on a dataset created by concatenating speakers from the TIMIT database, that is referred to as the TIMIT data set. A comparison between the performance of the three systems is made based on t-statistics
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
Multimodal information (e.g., visible and thermal) can generate robust
pedestrian detections to facilitate around-the-clock computer vision
applications, such as autonomous driving and video surveillance. However, it
still remains a crucial challenge to train a reliable detector working well in
different multispectral pedestrian datasets without manual annotations. In this
paper, we propose a novel unsupervised domain adaptation framework for
multispectral pedestrian detection, by iteratively generating pseudo
annotations and updating the parameters of our designed multispectral
pedestrian detector on target domain. Pseudo annotations are generated using
the detector trained on source domain, and then updated by fixing the
parameters of detector and minimizing the cross entropy loss without
back-propagation. Training labels are generated using the pseudo annotations by
considering the characteristics of similarity and complementarity between
well-aligned visible and infrared image pairs. The parameters of detector are
updated using the generated labels by minimizing our defined multi-detection
loss function with back-propagation. The optimal parameters of detector can be
obtained after iteratively updating the pseudo annotations and parameters.
Experimental results show that our proposed unsupervised multimodal domain
adaptation method achieves significantly higher detection performance than the
approach without domain adaptation, and is competitive with the supervised
multispectral pedestrian detectors
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
- …