212 research outputs found
A markovian approach to unsupervised change detection with multiresolution and multimodality SAR data
In the framework of synthetic aperture radar (SAR) systems, current satellite missions make it possible to acquire images at very high and multiple spatial resolutions with short revisit times. This scenario conveys a remarkable potential in applications to, for instance, environmental monitoring and natural disaster recovery. In this context, data fusion and change detection methodologies play major roles. This paper proposes an unsupervised change detection algorithmfor the challenging case of multimodal SAR data collected by sensors operating atmultiple spatial resolutions. The method is based on Markovian probabilistic graphical models, graph cuts, linear mixtures, generalized Gaussian distributions, Gram-Charlier approximations, maximum likelihood and minimum mean squared error estimation. It benefits from the SAR images acquired at multiple spatial resolutions and with possibly different modalities on the considered acquisition times to generate an output change map at the finest observed resolution. This is accomplished by modeling the statistics of the data at the various spatial scales through appropriate generalized Gaussian distributions and by iteratively estimating a set of virtual images that are defined on the pixel grid at the finest resolution and would be collected if all the sensors could work at that resolution. A Markov random field framework is adopted to address the detection problem by defining an appropriate multimodal energy function that is minimized using graph cuts
Polarimetric SAR Change Detection with the Complex Hotelling-Lawley Trace Statistic
Accepted manuscript version. Published version at http://dx.doi.org/10.1109/TGRS.2016.2532320.In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling-Lawley trace statistic for measuring the similarity of two covariance matrices. The distribution of the Hotelling-Lawley trace statistic is ap- proximated by a Fisher-Snedecor distribution, which is used to define the significance level of a false alarm rate regulated change detector. Experiments on simulated and real PolSAR data sets demonstrate that the proposed change detection method gives detections rates and error rates that are comparable with the generalized likelihood ratio test
An Adaptive Semi-Parametric and Context-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images
In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semi-parametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov Random Field (MRF) approach used to model the spatial-contextual information contained in the multitemporal images considered, a change detection map is generated. The adaptive semi-parametric nature of the proposed technique allows its application to different kinds of remote-sensing images. Experimental results, obtained on two sets of multitemporal remote-sensing images acquired by two different sensors, confirm the validity of the proposed approach
Analytic Expressions for Stochastic Distances Between Relaxed Complex Wishart Distributions
The scaled complex Wishart distribution is a widely used model for multilook
full polarimetric SAR data whose adequacy has been attested in the literature.
Classification, segmentation, and image analysis techniques which depend on
this model have been devised, and many of them employ some type of
dissimilarity measure. In this paper we derive analytic expressions for four
stochastic distances between relaxed scaled complex Wishart distributions in
their most general form and in important particular cases. Using these
distances, inequalities are obtained which lead to new ways of deriving the
Bartlett and revised Wishart distances. The expressiveness of the four analytic
distances is assessed with respect to the variation of parameters. Such
distances are then used for deriving new tests statistics, which are proved to
have asymptotic chi-square distribution. Adopting the test size as a comparison
criterion, a sensitivity study is performed by means of Monte Carlo experiments
suggesting that the Bhattacharyya statistic outperforms all the others. The
power of the tests is also assessed. Applications to actual data illustrate the
discrimination and homogeneity identification capabilities of these distances.Comment: Accepted for publication in the IEEE Transactions on Geoscience and
Remote Sensing journa
Detection of temporarily flooded vegetation using time series of dual polarised C-band synthetic aperture radar data
The intense research of the last decades in the field of flood monitoring has shown that microwave
sensors provide valuable information about the spatial and temporal flood extent. The new
generation of satellites, such as the Sentinel-1 (S-1) constellation, provide a unique, temporally
high-resolution detection of the earth's surface and its environmental changes. This opens up new
possibilities for accurate and rapid flood monitoring that can support operational applications. Due
to the observation of the earth's surface from space, large-scale flood events and their spatiotemporal changes can be monitored. This requires the adaptation of existing or the development of
new algorithms, which on the one hand enable precise and computationally efficient flood
detection and on the other hand can process a large amounts of data.
In order to capture the entire extent of the flood area, it is essential to detect temporary flooded
vegetation (TFV) areas in addition to the open water areas. The disregard of temporary flooded
vegetation areas can lead to severe underestimation of the extent and volume of the flood. Under
certain system and environmental conditions, Synthetic Aperture Radar (SAR) can be utilized to
extract information from under the vegetation cover. Due to multiple backscattering of the SAR
signal between the water surface and the vegetation, the flooded vegetation areas are mostly
characterized by increased backscatter values. Using this information in combination with a
continuous monitoring of the earth's surface by the S-1 satellites, characteristic time series-based
patterns for temporary flooded vegetation can be identified. This combination of information
provides the foundation for the time series approach presented here.
This work provides a comprehensive overview of the relevant sensor and environmental
parameters and their impact on the SAR signal regarding temporary open water (TOW) and TFV
areas. In addition, existing methods for the derivation of flooded vegetation are reviewed and their
benefits, limitations, methodological trends and potential research needs for this area are identified
and assessed. The focus of the work lies in the development of a SAR and time series-based
approach for the improved extraction of flooded areas by the supplementation of TFV and on the
provision of a precise and rapid method for the detection of the entire flood extent.
The approach developed in this thesis allows for the precise extraction of large-scale flood areas
using dual-polarized C-band time series data and additional information such as topography and
urban areas. The time series features include the characteristic variations (decrease and/or
increase of backscatter values) on the flood date for the flood-related classes compared to the
whole time series. These features are generated individually for each available polarization (VV,
VH) and their ratios (VV/VH, VV-VH, VV+VV). The generation of the time series features was
performed by Z-transform for each image element, taking into account the backscatter values on
the flood date and the mean value and standard deviation of the backscatter values from the nonflood dates. This allowed the comparison of backscatter intensity changes between the image
elements. The time series features constitute the foundation for the hierarchical threshold method
for deriving flood-related classes. Using the Random Forest algorithm, the importance of the time
series data for the individual flood-related classes was analyzed and evaluated. The results showed
that the dual-polarized time series features are particularly relevant for the derivation of TFV.
However, this may differ depending on the vegetation type and other environmental conditions.
The analyses based on S-1 data in Namibia, Greece/Turkey and China during large-scale floods
show the effectiveness of the method presented here in terms of classification accuracy. Theiv
supplementary integration of temporary flooded vegetation areas and the use of additional
information resulted in a significant improvement in the detection of the entire flood extent. It
could be shown that a comparably high classification accuracy (~ 80%) was achieved for the flood
extent in each of study areas. The transferability of the approach due to the application of a single
time series feature regarding the derivation of open water areas could be confirmed for all study
areas. Considering the seasonal component by using time series data, the seasonal variability of the
backscatter signal for vegetation can be detected. This allows for an improved differentiation
between flooded and non-flooded vegetation areas. Simultaneously, changes in the backscatter
signal can be assigned to changes in the environmental conditions, since on the one hand a time
series of the same image element is considered and on the other hand the sensor parameters do
not change due to the same acquisition geometry. Overall, the proposed time series approach
allows for a considerable improvement in the derivation of the entire flood extent by
supplementing the TOW areas with the TFV areas
Review on Active and Passive Remote Sensing Techniques for Road Extraction
Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe
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
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