15,326 research outputs found
Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks
Automatic urban land cover classification is a fundamental problem in remote
sensing, e.g. for environmental monitoring. The problem is highly challenging,
as classes generally have high inter-class and low intra-class variance.
Techniques to improve urban land cover classification performance in remote
sensing include fusion of data from different sensors with different data
modalities. However, such techniques require all modalities to be available to
the classifier in the decision-making process, i.e. at test time, as well as in
training. If a data modality is missing at test time, current state-of-the-art
approaches have in general no procedure available for exploiting information
from these modalities. This represents a waste of potentially useful
information. We propose as a remedy a convolutional neural network (CNN)
architecture for urban land cover classification which is able to embed all
available training modalities in a so-called hallucination network. The network
will in effect replace missing data modalities in the test phase, enabling
fusion capabilities even when data modalities are missing in testing. We
demonstrate the method using two datasets consisting of optical and digital
surface model (DSM) images. We simulate missing modalities by assuming that DSM
images are missing during testing. Our method outperforms both standard CNNs
trained only on optical images as well as an ensemble of two standard CNNs. We
further evaluate the potential of our method to handle situations where only
some DSM images are missing during testing. Overall, we show that we can
clearly exploit training time information of the missing modality during
testing
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
Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones
This paper proposes a novel framework for fusing multi-temporal,
multispectral satellite images and OpenStreetMap (OSM) data for the
classification of local climate zones (LCZs). Feature stacking is the most
commonly-used method of data fusion but does not consider the heterogeneity of
multimodal optical images and OSM data, which becomes its main drawback. The
proposed framework processes two data sources separately and then combines them
at the model level through two fusion models (the landuse fusion model and
building fusion model), which aim to fuse optical images with landuse and
buildings layers of OSM data, respectively. In addition, a new approach to
detecting building incompleteness of OSM data is proposed. The proposed
framework was trained and tested using data from the 2017 IEEE GRSS Data Fusion
Contest, and further validated on one additional test set containing test
samples which are manually labeled in Munich and New York. Experimental results
have indicated that compared to the feature stacking-based baseline framework
the proposed framework is effective in fusing optical images with OSM data for
the classification of LCZs with high generalization capability on a large
scale. The classification accuracy of the proposed framework outperforms the
baseline framework by more than 6% and 2%, while testing on the test set of
2017 IEEE GRSS Data Fusion Contest and the additional test set, respectively.
In addition, the proposed framework is less sensitive to spectral diversities
of optical satellite images and thus achieves more stable classification
performance than state-of-the art frameworks.Comment: accepted by TGR
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
Classification accuracy increase using multisensor data fusion
The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.)
but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the
confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification
products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral
data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since
this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed
for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution
SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and
multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised
clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network).
This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced
by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion
of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types
of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results
of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to other
established methods illustrates the advantage in the classification accuracy for many classes such as buildings, low vegetation, sport
objects, forest, roads, rail roads, etc
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