Bakioglua O.B. | Topan H. | Özendia M. | Cama A.
Konferans nesnesi | 2017 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives42 ( 4W6 ) , pp.27 - 29
Pan-sharpened images of RASAT and GÖKTÜRK-2 satellites were generated using High Pass Filter (HPF) in this paper. GÖKTÜRK-2 satellite has 11 bits radiometric resolution, 2.5 m GSD in panchromatic band and 5 m GSD in VNIR bands whereas RASAT has 8 bits radiometric resolution, 7.5 m GSD in panchromatic band and 15 m GSD in visible bands. Quantitative analysis was carried out by spatial metric while the while the products were qualitatively analysed with visual interpretation by an expert group. The values for spatial metric were estimated as 0.9678 and 0.9542 for RASAT and GÖKTÜRK-2, respectively. It can be concluded that the success . . .of HPF is almost satisfactory considering the optimal value of spatial metric is 1. The visual analysis shows the performance of GÖKTÜRK-2 is higher than RASAT since the higher radiometric and geometric resolution of GÖKTÜRK-2. All operations were run in SharpQ derived by the authors in Matlab environment. © Authors 2017 Daha fazlası Daha az
Topan H. | Büyüksalih G. | Jacobsen K.
Konferans nesnesi | 2004 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives35 , pp.27 - 29
The information contents of high resolution space images, usable for mapping, are not only depending upon the image resolution that means in case of digital data, depending upon the pixel size in the object space. Important is also the contrast, the spectral range, radiometric resolution and colour beside the atmospheric condition and the object contrast. From the area of Zonguldak, Turkey different space images are available like taken by IKONOS, KVR-1000, SPOT 5, IRS-1C, TK350, ASTER, Landsat TM, JERS and SRTM X-band. Of course the information content is mainly depending upon the pixel size on the ground, but this is still quite d . . .ifferent for the RADAR images taken by JERS and SRTM. The object identification in these images disturbed by speckle cannot be compared with optical images having the same pixel size. There is a rule of thumb for the relation of the pixel size to the possible map scale, but it cannot be used for ground pixels with a size exceeding 5m because this is leading to a loss of important information which must be available also in small scale maps. The limited radiometric resolution of IRS-1C images is still a disadvantage, especially in dark and shadow areas. The KVR-1000 available with 1.4m pixel size cannot be compared directly with the information contents which should be included with this resolution. The colour information of IKONOS supports the object identification, so the 4m ground pixel size includes a higher information contents like a panchromatic image with the same resolution and the object identification is quite easier. With IKONOS pan sharpened images maps up to a scale 1 : 7000 can be created. © 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved Daha fazlası Daha az
Calò F. | Notti D. | Galve J.P. | Abdikan S. | Görüm T. | Orhan O. | Makineci H.B.
Konferans nesnesi | 2018 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives42 ( 3W4 ) , pp.129 - 135
Groundwater depletion caused by rapid population growth, global climate change, water resources overexploitation is a major concern in many regions of the world. Consequences are not limited to a non-renewable water loss but extend to environmental degradation and geo-hazards risk increase. In areas where excessive groundwater withdrawal occurs, land subsidence induced by aquifer compaction is observed, resulting in severe socio-economic damage for the affected communities. In this work, we apply a multi-source data approach to investigate the fragile environment of Konya plain, central Turkey. The area, which is under strong anthro . . .pogenic pressures and faces with serious water-related problems, is widely affected by land subsidence. In order to analyze the spatial and temporal pattern of the subsidence process we use the Small BAseline Subset DInSAR technique to process two datasets of ENVISAT SAR images spanning the 2002-2010 period and to produce ground deformation maps and associated time-series. Results, complemented with meteorological, stratigraphic and piezometric data as well as with land-cover information, allow us to obtain a comprehensive picture of the climatic, hydrogeological and human dynamics of the study area. © Authors 2018. CC BY 4.0 License Daha fazlası Daha az
Büyüksalih G. | Baz I. | Alkan M. | Jacobsen K.
Konferans nesnesi | 2012 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives39 , pp.203 - 207
For planning purposes 42km coast line of the Black Sea, starting at the Bosporus going in West direction, with a width of approximately 5km, was imaged by WorldView-2. Three stereo scenes have been oriented at first by 3D-affine transformation and later by bias corrected RPC solution. The result is nearly the same, but it is limited by identification of the control points in the images. Nevertheless after blunder elimination by data snooping root mean square discrepancies below 1 pixel have been reached. The root mean square discrepancy at control point height reached 0.5m up to 1.3m with a base to height relation between 1:1.26 and . . . 1:1.80. Digital Surface models (DSM) with 4m spacing have been generated by least squares matching with region growing, supported by image pyramids. A higher percentage of the mountainous area is covered by forest, requiring the approximation based on image pyramids. In the forest area the approximation just by region growing leads to larger gaps in the DSM. Caused by the good image quality of WorldView-2 the correlation coefficients reached by least squares matching are high and even in most forest areas a satisfying density of accepted points was reached. Two stereo models have an overlapping area of 1.6 km times 6.7km allowing an accuracy evaluation. Small, but nevertheless significant differences in scene orientation have been eliminated by least squares shift of both overlapping height models to each other. The root mean square differences of both independent DSM are 1.06m or as a function of terrain inclination 0.74m + 0.55m* tangent (slope). The terrain inclination in the average is 7° with 12% exceeding 17°. The frequency distribution of height discrepancies is not far away from normal distribution, but as usual, larger discrepancies are more often available as corresponding to normal distribution. This also can be seen by the normalized medium absolute deviation (NMAS) related to 68% probability level of 0.83m being significant smaller as the root mean square differences. Nevertheless the results indicate a standard deviation of the single height models of 0.75m or 0.52m + 0.39* tangent (slope), corresponding to approximately 0.6 pixels for the x-parallax in flat terrain, being very satisfying for the available land cover. An interpolation over 10m enlarged the root mean square differences of both height models nearly by 50% Daha fazlası Daha az
Sefercik U.G. | Yastikli N. | Atalay C.
Konferans nesnesi | 2017 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives42 ( 2W7 ) , pp.641 - 646
In synthetic aperture radar (SAR) technology, urban mapping and modelling have become possible with revolutionary missions TerraSAR-X (TSX) and Cosmo-SkyMed (CSK) since 2007. These satellites offer 1m spatial resolution in high-resolution spotlight imaging mode and capable for high quality digital surface model (DSM) acquisition for urban areas utilizing interferometric SAR (InSAR) technology. With the advantage of independent generation from seasonal weather conditions, TSX and CSK DSMs are much in demand by scientific users. The performance of SAR DSMs is influenced by the distortions such as layover, foreshortening, shadow and do . . .uble-bounce depend up on imaging geometry. In this study, the potential of DSMs derived from convenient 1m high-resolution spotlight (HS) InSAR pairs of CSK and TSX is validated by model-to-model absolute and relative accuracy estimations in an urban area. For the verification, an airborne laser scanning (ALS) DSM of the study area was used as the reference model. Results demonstrated that TSX and CSK urban DSMs are compatible in open, built-up and forest land forms with the absolute accuracy of 8-10 m. The relative accuracies based on the coherence of neighbouring pixels are superior to absolute accuracies both for CSK and TSX Daha fazlası Daha az
Avsar N.B. | Kutoglu S.H.
Konferans nesnesi | 2019 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives42 ( 3/W8 ) , pp.43 - 47
Potential sea level rise poses a significant threat to low-lying areas. Considering present and future of coastal areas, scientific study of sea level rise is an essential for adapting to sea level extremes. In this study, the relative sea level change in the Black Sea were investigated using data of 12 tide-gauge and 6 GNSS stations. Results generally indicated sea level rise along the Black Sea coast. Only at Bourgas tide-gauge station, a sea level fall was detected. A significant sea level change were not determined at Sinop tide-gauge station. On the other hand, at some stations such as Poti and Sile, ground subsidence contribut . . .ion to relative sea level changes were observed. © 2019 International Society for Photogrammetry and Remote Sensing Daha fazlası Daha az
Becek K. | Boguslawski P.
Konferans nesnesi | 2018 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives42 ( 4 ) , pp.41 - 44
This paper discusses a current issue for several experimental science disciplines, which is the Big Data Problem (BDP). This research study focused on light intensity and ranging (LiDAR) datasets, which are collected for modelling spatial features found on the surface of the earth. Currently, LiDAR datasets are known to be extremely redundant for many applications. Using a formula that allows for calculating the variance of the target-induced error (so-called T-error) caused by the discretisation and quantisation of a 3D surface as a criterion for the quantitative assessment of the fidelity of a model, the use of a Q-tree-based spli . . .t of the surface is proposed for cells of various sizes depending on the fidelity requirements. A LiDAR dataset representing a 1 km x 1 km terrain surface tile using approximately 12 x 106 points was used during the experiments. The initial LiDAR dataset was used to produce a digital terrain model (DTM) at a 0.5 m x 0.5 m resolution, which was used as a reference model. Subsequently, the initial LiDAR dataset was decimated at various rates, and the resulting DTMs were compared with the reference model. The Q-tree based data structure was utilised to illustrate that the Q-tree approach allows for the production of DTMs at a ‘controlled’ fidelity with a considerable reduction in data volume. © Authors 2018 Daha fazlası Daha az
Sefercik U.G. | Soergel U.
Konferans nesnesi | 2014 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives40 ( 7 ) , pp.155 - 160
In recent years, interferometric sytnthetic aperture radar (InSAR) is one of the most preferred techniques to generate digital surface models (DSM) which are the three dimensional (3D) digital cartographic representations of earth surface including all terrain and non-terrain formations. Interferometric DSM generation using synthetic aperture radar (SAR) imagery is not an easy process and the vertical absolute accuracy of the final product depends on various parameters. In this study, we aimed to demonstrate the influence of temporal baseline between SAR image-pairs on the vertical absolute accuracy of high resolution interferometri . . .c DSMs. The application was realized covering 20km2 area in Berlin, Germany using 15 descending orbit high resolution spotlight (HS) TerraSAR-X (TSX) images. The suitable interferometric pairs were determined for DSM generation and two of them that have similar parameters except temporal baseline were selected regarding the purposes of the study. The master image was selected as same in the generation of both DSMs and the temporal baselines between this master image and slave images were 11 days (1 period) and 187 days (17 periods), respectively. TSX HS DSMs were generated with 2 m grid spacing and the vertical absolute accuracies were calculated based on the comparison with a reference DSM generated by radargrammetry. The analyses were realized for built-up and forest land classes separately. The results proved that longer temporal baseline has negative influence on the vertical absolute accuracies of TSX HS interferometric DSMs. The first DSM which has the shortest temporal baseline, possible for TSX sensing is better than the second one as approx. 1.5m both for built-up and forest areas Daha fazlası Daha az
Jacobsen K. | Topan H. | Cam A. | Özendi M. | Oruc M.
Konferans nesnesi | 2014 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives40 ( 1 ) , pp.173 - 177
Pleiades images are distributed with 50cm ground sampling distance (GSD) even if the physical resolution for nadir images is just 70cm. By theory this should influence the effective GSD determined by means of point spread function at image edges. Nevertheless by edge enhancement the effective GSD can be improved, but this should cause enlarged image noise. Again image noise can be reduced by image restoration. Finally even optimized image restoration cannot improve the image information from 70cm to 50cm without loss of details, requiring a comparison of Pleiades image details with other very high resolution space images. The image . . .noise has been determined by analysis of the whole images for any sub-area with 5 pixels times 5 pixels. Based on the standard deviation of grey values in the small sub-areas the image noise has been determined by frequency analysis. This leads to realistic results, checked by test targets. On the other hand the visual determination of image noise based on apparently homogenous sub-areas results in too high values because the human eye is not able to identify small grey value differences - it is limited to just approximately 40 grey value steps over the available gray value range, so small difference in grey values cannot be seen, enlarging results of a manual noise determination. A tri-stereo combination of Pleiades 1A in a mountainous, but partially urban, area has been analyzed and compared with images of the same area from WorldView-1, QuickBird and IKONOS. The image restoration of the Pleiades images is very good, so the effective image resolution resulted in a factor 1.0, meaning that the effective resolution corresponds to the nominal resolution of 50cm. This does not correspond to the physical resolution of 70cm, but by edge enhancement the steepness of the grey value profile across the edge can be enlarged, reducing the width of the point spread function. Without additional filtering edge enhancement enlarges the image noise, but the average image noise of approximately 1.0 grey values related to 8bit images is very small, not indicating the edge enhancement and the down sampling of the GSD from 70cm to 50cm. So the direct comparison with the other images has to give the answer if the image quality of Pleiades images is on similar level as corresponding to the nominal resolution. As expected with the image geometry there is no problem. This is the case for all used space images in the test area, where the point identification limits the accuracy of the scene orientation Daha fazlası Daha az
Ustuner M. | Sanli F.B. | Abdikan S. | Esetlili M.T. | Bilgin G.
Konferans nesnesi | 2018 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives42 ( 1 ) , pp.451 - 456
Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (?¯) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) H?¯, (2) H?¯Span, (3) H?¯A, (4) H?¯ASpan and (5) coherency [T] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 Jun . . .e, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that H?ASpan (91.43% for SVM, 92.25% for RF and 90.55% for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25% by RF and H?ASpan while lowest classification accuracy was obtained as 66.99% by NB and H?. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification. © Authors 2018. CC BY 4.0 License Daha fazlası Daha az
Avsar N.B. | Kutoglu S.H. | Jin S. | Erol B.
Konferans nesnesi | 2015 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives40 ( 1W5 ) , pp.67 - 71
In this study, we focus on sea level changes along the Black Sea coast. For this purpose, at same observation period the linear trends and the components of seasonal variations of sea level change are estimated at 12 tide gauge sites (Amasra, Igneada, Trabzon-II, Sinop, Sile, Poti, Batumi, Sevastopol, Tuapse, Varna, Bourgas, and Constantza) located along the Black Sea coast and available altimetric grid points closest to the tide gauge locations. The consistency of the results derived from both observations are investigated and interpreted. Furthermore, in order to compare the trends at the same location, it is interpolated from the . . . trends obtained at the altimetric grid points in the defined neighbouring area with a diameter of 0.125° using a weighted average interpolation algorithm at each tide gauge site. For some tide gauges such as Sevastopol, Varna, and Bourgas, it is very likely that the trend estimates are not reliable because the time-spans overlapping the altimeter period are too short. At Sile, the long-term change for the time series of both data types do not give statistically significant linear rates. However, when the sites have long-term records, a general agreement between the satellite altimetry and tide gauge time series is observed at Poti (~20 years) and Tuapse (~18 years). On the other hand, the difference of annual phase between satellite altimetry and tide gauge results is from 1.32° to 71.48° Daha fazlası Daha az
Ustuner M. | Sanli F.B. | Abdikan S.
Konferans nesnesi | 2016 | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives41 , pp.379 - 384
The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines . . .(SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier Daha fazlası Daha az