389 research outputs found
Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs
This thesis is an outcome of the project âFlood and damage assessment using very high resolution SAR dataâ (SAR-HQ), which is embedded in the interdisciplinary oriented RIMAX (Risk Management of Extreme Flood Events) programme, funded by the Federal Ministry of Education and Research (BMBF). It comprises the results of three scientific papers on automatic near real-time flood detection in high resolution X-band synthetic aperture radar (SAR) satellite data for operational rapid mapping activities in terms of disaster and crisis-management support.
Flood situations seem to become more frequent and destructive in many regions of the world. A rising awareness of the availability of satellite based cartographic information has led to an increase in requests to corresponding mapping services to support civil-protection and relief organizations with disaster-related mapping and analysis activities. Due to the rising number of satellite systems with high revisit frequencies, a strengthened pool of SAR data is available during operational flood mapping activities. This offers the possibility to observe the whole extent of even large-scale flood events and their spatio-temporal evolution, but also calls for computationally efficient and automatic flood detection methods, which should drastically reduce the user input required by an active image interpreter.
This thesis provides solutions for the near real-time derivation of detailed flood parameters such as flood extent, flood-related backscatter changes as well as flood classification probabilities from the new generation of high resolution X-band SAR satellite imagery in a completely unsupervised way. These data are, in comparison to images from conventional medium-resolution SAR sensors, characterized by an increased intra-class and decreased inter-class variability due to the reduced mixed pixel phenomenon. This problem is addressed by utilizing multi-contextual models on irregular hierarchical graphs, which consider that semantic image information is less represented in single pixels but in homogeneous image objects and their mutual relation. A hybrid Markov random field (MRF) model is developed, which integrates scale-dependent as well as spatio-temporal contextual information into the classification process by combining hierarchical causal Markov image modeling on automatically generated irregular hierarchical graphs with noncausal Markov modeling related to planar MRFs. This model is initialized in an unsupervised manner by an automatic tile-based thresholding approach, which solves the flood detection problem in large-size SAR data with small a priori class probabilities by statistical parameterization of local bi-modal class-conditional density functions in a time efficient manner.
Experiments performed on TerraSAR-X StripMap data of Southwest England and ScanSAR data of north-eastern Namibia during large-scale flooding show the effectiveness of the proposed methods in terms of classification accuracy, computational performance, and transferability. It is further demonstrated that hierarchical causal Markov models such as hierarchical maximum a posteriori (HMAP) and hierarchical marginal posterior mode (HMPM) estimation can be effectively used for modeling the inter-spatial context of X-band SAR data in terms of flood and change detection purposes. Although the HMPM estimator is computationally more demanding than the HMAP estimator, it is found to be more suitable in terms of classification accuracy. Further, it offers the possibility to compute marginal posterior entropy-based confidence maps, which are used for the generation of flood possibility maps that express that the uncertainty in labeling of each image element. The supplementary integration of intra-spatial and, optionally, temporal contextual information into the Markov model results in a reduction of classification errors. It is observed that the application of the hybrid multi-contextual Markov model on irregular graphs is able to enhance classification results in comparison to modeling on regular structures of quadtrees, which is the hierarchical representation of images usually used in MRF-based image analysis.
X-band SAR systems are generally not suited for detecting flooding under dense vegetation canopies such as forests due to the low capability of the X-band signal to penetrate into media. Within this thesis a method is proposed for the automatic derivation of flood areas beneath shrubs and grasses from TerraSAR-X data. Furthermore, an approach is developed, which combines high resolution topographic information with multi-scale image segmentation to enhance the mapping accuracy in areas consisting of flooded vegetation and anthropogenic objects as well as to remove non-water look-alike areas
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
Sentinel-1 Satellite Data as a Tool for Monitoring Inundation Areas near Urban Areas in the Mexican Tropical Wet
This work shows advances in the field of water body monitoring with radar images. Particularly, a monitoring procedure is developed to define the extension and frequency of inundation for continental waters of the Grijalva-Usumacinta basin, in the state of Tabasco, Mexico. This is a region located in the Mexican tropical wet and under its meteorological conditions, radar technology can be used to characterize monthly inundation frequency. The identification of water bodies were obtained by processing images at a monthly intervals captured by Sentinel-1A during 2015 having kappa indices and overall accuracy higher than 0.9. The chapter describes the seasonal variability of these water bodies, and at the same time, the relationship with human settlements located in their neighborhood. To do this, a proximity analysis was carried out to emphasize the importance of spatial-temporal studies of superficial water bodies, linked to an urban and a rural area. This information is useful to investigate changes in the ecosystem, as well as risks to human settlements, and as a contribution for a comprehensive management of hydric resources
Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review
The coastal zone offers among the worldâs most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of landâ and waterârelated applications in coastal zones. Compared to optical satellites, cloudâcover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have allâweather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloudâprone tropical and subâtropical climates. The canopy penetration capability with long radar wavelength enables Lâband SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban
sprawl and climate changeâinduced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne Lâband SAR data for geoscientific
analyses that are relevant for coastal land applications
A multi-scale flood monitoring system based on fully automatic MODIS and TerraSAR-X processing chains
A two-component fully automated flood monitoring system is described and evaluated. This is a result of combining two individual flood services that are currently
under development at DLRâs (German Aerospace Center) Center for Satellite based Crisis Information (ZKI) to rapidly support disaster management activities. A first-phase monitoring component of the system systematically detects potential flood events on a
continental scale using daily-acquired medium spatial resolution optical data from the Moderate Resolution Imaging Spectroradiometer (MODIS). A threshold set controls the activation of the second-phase crisis component of the system, which derives flood information at higher spatial detail using a Synthetic Aperture Radar (SAR) based satellite mission (TerraSAR-X). The proposed activation procedure finds use in the identification of flood situations in different spatial resolutions and in the time-critical and on demand
programming of SAR satellite acquisitions at an early stage of an evolving flood situation. The automated processing chains of the MODIS (MFS) and the TerraSAR-X Flood Service (TFS) include data pre-processing, the computation and adaptation of global auxiliary data, thematic classification, and the subsequent dissemination of flood maps using an interactive web-client. The system is operationally demonstrated and evaluated via the monitoring two recent flood events in Russia 2013 and Albania/Montenegro 2013
Remote sensing satellite image processing techniques for image classification: a comprehensive survey
This paper is a brief survey of advance technological aspects
of Digital Image Processing which are applied to remote
sensing images obtained from various satellite sensors. In
remote sensing, the image processing techniques can be
categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification.
Image pre-processing is the initial processing which deals
with correcting radiometric distortions, atmospheric distortion
and geometric distortions present in the raw image data.
Enhancement techniques are applied to preprocessed data in
order to effectively display the image for visual interpretation.
It includes techniques to effectively distinguish surface
features for visual interpretation. Transformation aims to
identify particular feature of earthâs surface and classification
is a process of grouping the pixels, that produces effective
thematic map of particular land use and land cover
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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