556 research outputs found
Breaking new ground in mapping human settlements from space -The Global Urban Footprint-
Today 7.2 billion people inhabit the Earth and by 2050 this number will have
risen to around nine billion, of which about 70 percent will be living in
cities. Hence, it is essential to understand drivers, dynamics, and impacts of
the human settlements development. A key component in this context is the
availability of an up-to-date and spatially consistent map of the location and
distribution of human settlements. It is here that the Global Urban Footprint
(GUF) raster map can make a valuable contribution. The new global GUF binary
settlement mask shows a so far unprecedented spatial resolution of 0.4 arcsec
() that provides - for the first time - a complete picture of the
entirety of urban and rural settlements. The GUF has been derived by means of a
fully automated processing framework - the Urban Footprint Processor (UFP) -
that was used to analyze a global coverage of more than 180,000 TanDEM-X and
TerraSAR-X radar images with 3m ground resolution collected in 2011-2012.
Various quality assessment studies to determine the absolute GUF accuracy based
on ground truth data on the one hand and the relative accuracies compared to
established settlements maps on the other hand, clearly indicate the added
value of the new global GUF layer, in particular with respect to the
representation of rural settlement patterns. Generally, the GUF layer achieves
an overall absolute accuracy of about 85\%, with observed minima around 65\%
and maxima around 98 \%. The GUF will be provided open and free for any
scientific use in the full resolution and for any non-profit (but also
non-scientific) use in a generalized version of 2.8 arcsec ().
Therewith, the new GUF layer can be expected to break new ground with respect
to the analysis of global urbanization and peri-urbanization patterns,
population estimation or vulnerability assessment
Improved POLSAR Image Classification by the Use of Multi-Feature Combination
Polarimetric SAR (POLSAR) provides a rich set of information about objects on land surfaces. However, not all information works on land surface classification. This study proposes a new, integrated algorithm for optimal urban classification using POLSAR data. Both polarimetric decomposition and time-frequency (TF) decomposition were used to mine the hidden information of objects in POLSAR data, which was then applied in the C5.0 decision tree algorithm for optimal feature selection and classification. Using a NASA/JPL AIRSAR POLSAR scene as an example, the overall accuracy and kappa coefficient of the proposed method reached 91.17% and 0.90 in the L-band, much higher than those achieved by the commonly applied Wishart supervised classification that were 45.65% and 0.41. Meantime, the overall accuracy of the proposed method performed well in both C- and P-bands. Polarimetric decomposition and TF decomposition all proved useful in the process. TF information played a great role in delineation between urban/built-up areas and vegetation. Three polarimetric features (entropy, Shannon entropy, T11 Coherency Matrix element) and one TF feature (HH intensity of coherence) were found most helpful in urban areas classification. This study indicates that the integrated use of polarimetric decomposition and TF decomposition of POLSAR data may provide improved feature extraction in heterogeneous urban areas
A Novel Technique Based on the Combination of Labeled Co-Occurrence Matrix and Variogram for the Detection of Built-up Areas in High-Resolution SAR Images
Interests in synthetic aperture radar (SAR) data analysis is driven by the constantly increased spatial resolutions of the acquired images, where the geometries of scene objects can be better defined than in lower resolution data. This paper addresses the problem of the built-up areas extraction in high-resolution (HR) SAR images, which can provide a wealth of information to characterize urban environments. Strong backscattering behavior is one of the distinct characteristics of built-up areas in a SAR image. However, in practical applications, only a small portion of pixels characterizing the built-up areas appears bright. Thus, specific texture measures should be considered for identifying these areas. This paper presents a novel texture measure by combining the proposed labeled co-occurrence matrix technique with the specific spatial variability structure of the considered land-cover type in the fuzzy set theory. The spatial variability is analyzed by means of variogram, which reflects the spatial correlation or non-similarity associated with a particular terrain surface. The derived parameters from the variograms are used to establish fuzzy functions to characterize the built-up class and non built-up class, separately. The proposed technique was tested on TerraSAR-X images acquired of Nanjing (China) and Barcelona (Spain), and on a COSMO-SkyMed image acquired of Hangzhou (China). The obtained classification accuracies point out the effectiveness of the proposed technique in identifying and detecting built-up areas
Towards a 20m global building map from Sentinel-1 SAR Data
This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.Peer ReviewedPostprint (published version
Levee Slide Detection using Synthetic Aperture Radar Magnitude and Phase
The objectives of this research are to support the development of state-of-the-art methods using remotely sensed data to detect slides or anomalies in an efficient and cost-effective manner based on the use of SAR technology. Slough or slump slides are slope failures along a levee, which leave areas of the levee vulnerable to seepage and failure during high water events. This work investigates the facility of detecting the slough slides on an earthen levee with different types of polarimetric Synthetic Aperture Radar (polSAR) imagery. The source SAR imagery is fully quad-polarimetric L-band data from the NASA Jet Propulsion Laboratoryâs (JPLâs) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area encompasses a portion of the levees of the lower Mississippi river, located in Mississippi, United States. The obtained classification results reveal that the polSAR data unsupervised classification with features extraction produces more appropriate results than the unsupervised classification with no features extraction. Obviously, supervised classification methods provide better classification results compared to the unsupervised methods. The anomaly identification is good with these results and was improved with the use of a majority filter. The classification accuracy is further improved with a morphology filter. The classification accuracy is significantly improved with the use of GLCM features. The classification results obtained for all three cases (magnitude, phase, and complex data), with classification accuracies for the complex data being higher, indicate that the use of synthetic aperture radar in combination with remote sensing imagery can effectively detect anomalies or slides on an earthen levee. For all the three samples it consistently shows that the accuracies for the complex data are higher when compared to those from the magnitude and phase data alone. The tests comparing complex data features to magnitude and phase data alone, and full complex data, and use of post-processing filter, all had very high accuracy. Hence we included more test samples to validate and distinguish results
Klassificering av marktÀcke med multi-temporal SAR och optisk satellitdata
Satellite data are widely used within remote sensing to respond to the growing need for a deeper understanding of the Earthâs bio- and geophysical parameters. Applications, such as land cover classification has for long been an important task within the field. Optical satellite data have proven to be efficient tools, however, they are unavailable in some conditions, such as cloudy weather. This deficit can be addressed with synthetic aperture radars (SAR), and recently, improvements have been made in their spatial and temporal coverage. Furthermore, a fusion of these data takes advantage of their different characteristics and can lead to even improved outcomes. The aim of this study was to develop and implement an effective land cover classification approach for the boreal forest zone by using multi-temporal SAR and optical data.
Optical and SAR satellite data were collected from the area around HyytiÀlÀ, Finland. One Landsat 8 scene and a time series of Sentinel-1 data spanning over a year were used. Co- and cross-polarized data were available. A very high resolution (VHR) reference image was manually interpreted to form training and test data. Features were extracted from both data sets and those from the SAR data were reduced using feature selection. A land cover classification was then performed separately on each data set and with a fused data set. Different features were tested to find an optimal combination. The classifications were performed with the nearest neighbor rule and the maximum likelihood classifier. This resulted in several classification maps which were validated with the test plots.
The results showed that the single-sensor classifications were noisy. Classifications with only optical imagery performed better. Additionally, removing some of the original data from the calculations, which can speed up the process, led to worse results. The multi-sensor classifications with the fused data improved the results significantly. Much of the noise was no longer present. The best classification was reached with a fused data set of four SAR features from VH polarized data and four optical features, which gained a final accuracy of 89.8 %. This classification was done with the maximum likelihood classifier. Accuracies up to 97.3 % were also reached but this result had clear flaws in the visual interpretation. It was concluded that fusing optical and SAR data for land cover classification in the boreal zone is a very promising strategy and should be investigated further to reach even better results.Satellitdata anvÀnds i stor utstrÀckning inom fjÀrranalys för att fylla det stÀndiga behovet av mer ingÄende kÀnnedom av jordens bio- och geofysiska parametrar. Applikationer, sÄsom klassificering av marktÀcke, har redan lÀnge varit en viktig uppgift inom studieomrÄdet. Optisk satellitdata har visat sig vara ett effektivt redskap, men den Àr inte tillgÀnglig i vissa situationer, sÄsom molnigt vÀder. Denna brist kan övervinnas med syntetisk aperturradar (SAR) och nyligen har förbÀttringar skett inom den spatiala och temporala tÀckningen. DÀrutöver utnyttjar en fusion av dessa data de olika karaktÀrerna vilket kan leda till Àven förbÀttrade resultat. Denna studies syfte var att utveckla och tillÀmpa en effektiv metod för klassificering av marktÀcke inom boreala skogar med hjÀlp av multi-temporal SAR och optisk data.
Optisk och SAR data samlades frÄn omrÄdet omkring HyytiÀlÀ, Finland. En Landsat 8 scen och en tidsserie av Sentinel-1 data över ett Är anvÀndes. Data med olika polarisationer var tillgÀngliga. En referensbild med hög resolution tolkades manuellt för att bilda trÀnings- och testdata. Variabler togs fram frÄn bÄda datauppsÀttningarna varefter variablerna frÄn SAR datan reducerades genom att vÀlja de bÀsta. En klassificering av marktÀcket utfördes sedan skiljt för de olika datauppsÀttningarna samt med sammanslagen data. Olika variabler testades för att hitta den bÀsta kombinationen. Klassificeringen gjordes med regeln för den nÀrmaste grannen samt med maximum likelihood-metoden. Detta resulterade i flera klassificeringskartor vilka sedan validerades med testdatan.
Resultaten av klassificeringarna med data frÄn en sensor hade mycket störningar. Den optiska bilden gav bÀttre resultat. DÄ en del av den ursprungliga datan togs bort frÄn utrÀkningarna, vilket kan effektivera processen, blev resultaten sÀmre. En fusion av datan förbÀttrade resultaten betydligt dÄ en stor del av störningarna försvann. Den bÀsta klassificeringen nÄddes med en sammanslagen datauppsÀttning av fyra SAR-variabler frÄn VH polariserad data och fyra optiska variabler med en slutlig noggrannhet pÄ 89.8 %. Denna klassificering gjordes med maximum likelihood-metoden. Noggrannheter upp till 97.3 % nÄddes Àven, men detta resultat hade stora brister i den visuella tolkningen. En slutsats drogs att en sammanslagning av optisk och SAR data för klassificering av marktÀcke i boreala omrÄden Àr en vÀldigt lovande strategi och bör undersökas vidare för att nÄ Àven bÀttre resultat
Assessment of high resolution SAR imagery for mapping floodplain water bodies: a comparison between Radarsat-2 and TerraSAR-X
Flooding is a world-wide problem that is considered as one of the most devastating natural hazards. New commercially available high spatial resolution Synthetic Aperture RADAR satellite imagery provides new potential for flood mapping. This research provides a quantitative assessment of high spatial resolution RADASAT-2 and TerraSAR-X products for mapping water bodies in order to help validate products that can be used to assist flood disaster management. An area near Dhaka in Bangladesh is used as a test site because of the large number of water bodies of different sizes and its history of frequent flooding associated with annual monsoon rainfall. Sample water bodies were delineated in the field using kinematic differential GPS to train and test automatic methods for water body mapping. SAR sensors products were acquired concurrently with the field visits; imagery were acquired with similar polarization, look direction and incidence angle in an experimental design to evaluate which has best accuracy for mapping flood water extent.
A methodology for mapping water areas from non-water areas was developed based on radar backscatter texture analysis. Texture filters, based on Haralick occurrence and co-occurrence measures, were compared and images classified using supervised, unsupervised and contextual classifiers. The evaluation of image products is based on an accuracy assessment of error matrix method using randomly selected ground truth data. An accuracy comparison was performed between classified images of both TerraSAR-X and Radarsat-2 sensors in order to identify any differences in mapping floods. Results were validated using information from field inspections conducted in good conditions in February 2009, and applying a model-assisted difference estimator for estimating flood area to derive Confidence Interval (CI) statistics at the 95% Confidence Level (CL) for the area mapped as water. For Radarsat-2 Ultrafine, TerraSAR-X Stripmap and Spotlight imagery, overall classification accuracy was greater than 93%. Results demonstrate that small water bodies down to areas as small as 150mÂČ can be identified routinely from 3 metre resolution SAR imagery. The results further showed that TerraSAR-X stripmap and spotlight images have better overall accuracy than RADARSAT-2 ultrafine beam modes images. The expected benefits of the research will be to improve the provision of data to assess flood risk and vulnerability, thus assisting in disaster management and post-flood recovery
Remote Sensing for NonâTechnical Survey
This chapter describes the research activities of the Royal Military Academy on remote sensing applied to mine action. Remote sensing can be used to detect specific features that could lead to the suspicion of the presence, or absence, of mines. Work on the automatic detection of trenches and craters is presented here. Land cover can be extracted and is quite useful to help mine action. We present here a classification method based on Gabor filters. The relief of a region helps analysts to understand where mines could have been laid. Methods to be a digital terrain model from a digital surface model are explained. The special case of multiâspectral classification is also addressed in this chapter. Discussion about data fusion is also given. Hyperâspectral data are also addressed with a change detection method. Synthetic aperture radar data and its fusion with optical data have been studied. Radar interferometry and polarimetry are also addressed
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