1,648 research outputs found
State-of-the-art and gaps for deep learning on limited training data in remote sensing
Deep learning usually requires big data, with respect to both volume and
variety. However, most remote sensing applications only have limited training
data, of which a small subset is labeled. Herein, we review three
state-of-the-art approaches in deep learning to combat this challenge. The
first topic is transfer learning, in which some aspects of one domain, e.g.,
features, are transferred to another domain. The next is unsupervised learning,
e.g., autoencoders, which operate on unlabeled data. The last is generative
adversarial networks, which can generate realistic looking data that can fool
the likes of both a deep learning network and human. The aim of this article is
to raise awareness of this dilemma, to direct the reader to existing work and
to highlight current gaps that need solving.Comment: arXiv admin note: text overlap with arXiv:1709.0030
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
The arithmetic recursive average as an instance of the recursive weighted power mean
The aggregation of multiple information sources has a long history and ranges from sensor fusion to the aggregation of individual algorithm outputs and human knowledge. A popular approach to achieve such aggregation is the fuzzy integral (FI) which is defined with respect to a fuzzy measure (FM (i.e. a normal, monotone capacity). In practice, the discrete FI aggregates information contributed by a discrete number of sources through a weighted aggregation (post-sorting), where the weights are captured by a FM that models the typically subjective ‘worth’ of subsets of the overall set of sources. While the combination of FI and FM has been very successful, challenges remain both in regards to the behavior of the resulting aggregation operators—which for example do not produce symmetrically mirrored outputs for symmetrically mirrored inputs—and also in a manifest difference between the intuitive interpretation of a stand-alone FM and its actual role and impact when used as part of information fusion with a FI. This paper elucidates these challenges and introduces a novel family of recursive average (RAV) operators as an alternative to the FI in aggregation with respect to a FM; focusing specifically on the arithmetic recursive average. The RAV is designed to address the above challenges, while also facilitating fine-grained analysis of the resulting aggregation of different combinations of sources. We provide the mathematical foundations of the RAV and include initial experiments and comparisons to the FI for both numeric and interval-valued data
Data-informed fuzzy measures for fuzzy integration of intervals and fuzzy numbers
The fuzzy integral (FI) with respect to a fuzzy measure (FM) is a powerful means of aggregating information. The most popular FIs are the Choquet and Sugeno, and most research focuses on these two variants. The arena of the FM is much more populated, including numerically derived FMs such as the Sugeno λ-measure and decomposable measure, expert-defined FMs, and data-informed FMs. The drawback of numerically derived and expert-defined FMs is that one must know something about the relative values of the input sources. However, there are many problems where this information is unavailable, such as crowdsourcing. This paper focuses on data-informed FMs, or those FMs that are computed by an algorithm that analyzes some property of the input data itself, gleaning the importance of each input source by the data they provide. The original instantiation of a data-informed FM is the agreement FM, which assigns high confidence to combinations of sources that numerically agree with one another. This paper extends upon our previous work in datainformed FMs by proposing the uniqueness measure and additive measure of agreement for interval-valued evidence. We then extend data-informed FMs to fuzzy number (FN)-valued inputs. We demonstrate the proposed FMs by aggregating interval and FN evidence with the Choquet and Sugeno FIs for both synthetic and real-world data
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection
A significant challenge in object detection is accurate identification of an
object's position in image space, whereas one algorithm with one set of
parameters is usually not enough, and the fusion of multiple algorithms and/or
parameters can lead to more robust results. Herein, a new computational
intelligence fusion approach based on the dynamic analysis of agreement among
object detection outputs is proposed. Furthermore, we propose an online versus
just in training image augmentation strategy. Experiments comparing the results
both with and without fusion are presented. We demonstrate that the augmented
and fused combination results are the best, with respect to higher accuracy
rates and reduction of outlier influences. The approach is demonstrated in the
context of cone, pedestrian and box detection for Advanced Driver Assistance
Systems (ADAS) applications.Comment: 21 pages, 12 figures, journal paper, MDPI Sensors, 201
Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks
Information fusion is an essential part of numerous engineering systems and
biological functions, e.g., human cognition. Fusion occurs at many levels,
ranging from the low-level combination of signals to the high-level aggregation
of heterogeneous decision-making processes. While the last decade has witnessed
an explosion of research in deep learning, fusion in neural networks has not
observed the same revolution. Specifically, most neural fusion approaches are
ad hoc, are not understood, are distributed versus localized, and/or
explainability is low (if present at all). Herein, we prove that the fuzzy
Choquet integral (ChI), a powerful nonlinear aggregation function, can be
represented as a multi-layer network, referred to hereafter as ChIMP. We also
put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient
descent-based optimization in light of the exponential number of ChI inequality
constraints. An additional benefit of ChIMP/iChIMP is that it enables
eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP
is applied to the fusion of a set of heterogeneous architecture deep models in
remote sensing. We show an improvement in model accuracy and our previously
established XAI indices shed light on the quality of our data, model, and its
decisions.Comment: IEEE Transactions on Fuzzy System
Introducing Fuzzy Layers for Deep Learning
Many state-of-the-art technologies developed in recent years have been
influenced by machine learning to some extent. Most popular at the time of this
writing are artificial intelligence methodologies that fall under the umbrella
of deep learning. Deep learning has been shown across many applications to be
extremely powerful and capable of handling problems that possess great
complexity and difficulty. In this work, we introduce a new layer to deep
learning: the fuzzy layer. Traditionally, the network architecture of neural
networks is composed of an input layer, some combination of hidden layers, and
an output layer. We propose the introduction of fuzzy layers into the deep
learning architecture to exploit the powerful aggregation properties expressed
through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals.
To date, fuzzy approaches taken to deep learning have been through the
application of various fusion strategies at the decision level to aggregate
outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16,
GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown
to improve accuracy performance for image classification tasks, none have
explored the use of fuzzified intermediate, or hidden, layers. Herein, we
present a new deep learning strategy that incorporates fuzzy strategies into
the deep learning architecture focused on the application of semantic
segmentation using per-pixel classification. Experiments are conducted on a
benchmark data set as well as a data set collected via an unmanned aerial
system at a U.S. Army test site for the task of automatic road segmentation,
and preliminary results are promising.Comment: 6 pages, 4 figures, published in 2019 IEEE International Conference
on Fuzzy Systems (FUZZ-IEEE
- …