Fusing image representations for classification using support vector machines

In order to improve classification accuracy different image representations are usually combined. This can be done by using two different fusing schemes. In feature level fusion schemes, image representations are combined before the classification process. In classifier fusion, the decisions taken separately based on individual representations are fused to make a decision. In this paper the main methods derived for both strategies are evaluated. Our experimental results show that classifier fusion performs better. Specifically Bayes belief integration is the best performing strategy for image classification task.Comment: Image and Vision Computing New Zealand, 2009. IVCNZ '09. 24th International Conference, Wellington : Nouvelle-Z\'elande (2009

Integrating spatiotemporal dynamics of natural capital security and urban ecosystem carbon metabolism

The purpose of the study is to address and quantify the increase in urban expansion and carbon (C) metabolism burden on ecosystem service value (ESV), net ecosystem productivity (NEP), and C storage of urban footprint. Urban footprint is required to meet the demands arising from economic consumption and production as well as waste accumulation and assimilation. Spatiotemporal changes in main land covers (LCs) were detected using remotely sensed data (Landsat 5 and 8, and digital elevation model) between 1987 and 2016. Changes in ESV and C influx, efflux and pools associated with LC dynamics were approximated using global proxies for a western Mediterranean region in Turkey of 54,162 km2. Urban expansion over the 29-year period decreased ESV by 22% ($7.28 ± 0.4 billion), NEP by 4.3% (2.3 ± 9 Gg C), and total ecosystem C pool by 10.9% (1008.3 ± 1006 Gg C) and led to a 62.8% appropriation of the total NEP (50.1 ± 51 Gg C) of the urban footprint in 2016. The main cause of the environmental degradation across the study region was the loss of the seminatural areas. Our findings emphasize that the deterioration rate of ecosystems should be slowed down by natural capital-friendly decisions and should not exceed rehabilitation rate of damaged ecosystems in the face of rapidly increasing burdens of the cities on their footprint.Izmir Institute of Technology and Abant Izzet Baysal Universit

A robust nonlinear scale space change detection approach for SAR images

In this paper, we propose a change detection approach based on nonlinear scale space analysis of change images for robust detection of various changes incurred by natural phenomena and/or human activities in Synthetic Aperture Radar (SAR) images using Maximally Stable Extremal Regions (MSERs). To achieve this, a variant of the log-ratio image of multitemporal images is calculated which is followed by Feature Preserving Despeckling (FPD) to generate nonlinear scale space images exhibiting different trade-offs in terms of speckle reduction and shape detail preservation. MSERs of each scale space image are found and then combined through a decision level fusion strategy, namely "selective scale fusion" (SSF), where contrast and boundary curvature of each MSER are considered. The performance of the proposed method is evaluated using real multitemporal high resolution TerraSAR-X images and synthetically generated multitemporal images composed of shapes with several orientations, sizes, and backscatter amplitude levels representing a variety of possible signatures of change. One of the main outcomes of this approach is that different objects having different sizes and levels of contrast with their surroundings appear as stable regions at different scale space images thus the fusion of results from scale space images yields a good overall performance

A region-based target detection method for SAR images (SAR görüntüleri için bölge tabanlı bir hedef tespit yöntemi)

Automatic target detection methods for synthetic aperture radar (SAR) images are sensitive to image resolution, size of the target to be detected, clutter complexity, and speckle noise level. A robust automatic target detection method needs to be less sensitive to the above factors. In this study, a constant false alarm rate (CFAR) based automatic target detection method which can find a target and its heterogeneous clutter independent of the image resolution and the target size has been developed. The proposed method provides efficient memory usage and low computational complexity

Sentetik açıklıklı radar görüntülerinde alan tabanlı hedef tespiti ve paralel gerçekleştirmesi (Region based target detection in synthetic aperture radar images and its parallel implementation)

Sentetik açıklıklı radar (SAR) görüntülerinde otomatik hedef tespiti yöntemleri görüntünün çözünürlüğüne, hedefin büyüklüğüne, parazit yankı karmaşıklığına ve benek gürültü seviyesine duyarlıdır. Gürbüz bir hedef tespiti yönteminin ise bu tür etkenlere daha az duyarlı olması istenir. Önerilen yöntem görüntünün öznitelik korumalı benek gürültü arındırma (feature preserving despeckling, FPD) yönteminden geçmiş hali üzerinden olası hedef bölgelerinin ve etrafındaki parazit yankı karmaşıklığının bulunması ve sabit yanlış alarm oranı elde edilecek şekilde eşiklenmesi esasına dayanmaktadır. Hesaplama verimliği OpenMP ve NVidia CUDA kullanılarak arttırılmış ve elde edilen hızlanmalar gösterilmiştir

Interactive ship segmentation in SAR images (SAR görüntülerinde etkileşimli gemi bölütleme)

Ship detection from synthetic aperture radar (SAR) images is important for various automatic target recognition (ATR) tasks. Although the ships in offshore areas can be easily detected, the ones near the shores or close to each other are difficult to detect. Furthermore, segmentation and classification of such ships is extremely difficult. In this study, a novel approach is presented for the fast and accurate segmentation of ship boundaries with minimal user interaction. In this approach, the rough location and orientation of a ship is determined by the user. Then, a ship model, which is constructed from synthetic ship images, is fitted on to the ship selected by the user and accurate ship boundaries are extracted. The effectiveness of the proposed algorithm is demonstrated by experimental results

Estimation of leakage from precipitation in aquifer using borehole data: Aalaşehir Basin (Manisa)

71.Türkiye Jeoloji Kurultayı, 23-27 Nisan 2018, AnkaraSon yıllarda iklim değişikliğine bağlı su kullanımının artması ve buna ek olarak aşırı nüfus artışı, yeraltısuyu kullanımını arttırmakta ve su kaynaklarını tehdit etmektedir. Su kaynaklarının sürdürülebilirliğinin sağlanması için barajlar ve yapay göller inşa edilmekte, ancak bu yapıların geniş yüzey alanlarında çok fazla buharlaşma görülmektedir. Dolayısı ile yeraltı suyunun, buharlaşma kayıplarının yaşanmadığı direkt yağış ile beslenen yapay beslenmesi önem kazanmaktadır. Yapay beslenme, havzalardaki suyu fazla buharlaşmadan korur ve su kaynaklarının potansiyelini artırıp, sürdürülebilirliğini sağlar. Bu çalışma kapsamında nümerik metotlar kullanılarak, Alaşehir (Manisa) havzasında alüvyon akiferin beslenmesinin belirlenmesi amaçlanmıştır. Türkiye’nin batısında yer alan ve ülkenin %2’sini oluşturan Gediz Havzası, önemli bir yeraltı suyu potansiyelinin olduğu ve kullanıldığı alan konumundadır.TUBITAK project number of 115Y06

Investigation of groundwater recharges mechanism in Alasehir plain: From physical characterization to modelling

IWA 2nd Regional Symposium on water, wastewater and environment, 22-24 March 2017, Cesme, İzmirCharacteristics of aquifer that allows the flow of groundwater, recharge and discharge mechanism effect the available groundwater potential. In order to determine the available potential of groundwater, the budget components are the most important parameters. In general, different analogical methods are applied in different countries, instead of using measurable data. Determination of the groundwater recharge is the most difficult parameter to be measured among the hydrological budget parameters. In general, the analogical methods are insufficient to determine the groundwater recharge rate. Precipitation, evaporation-transportation and runoff can be measured from the surface meteorological station. However, estimation of groundwater recharge cannot be measured directly. At the moment of climate change, visualization of the spatial distribution of the groundwater recharge estimation and mapping are needed. Therefore, improvements of easy groundwater recharge techniques are very important tools for groundwater basins to develop the water management planning.The Scientific and Technological Research Council of Turkey (TUBITAK), project no. 115Y06

Multi-risk interpretation of natural hazards for settlements of the Hatay province in the east Mediterranean region, Turkey using SRTM DEM

Many scientists have recently alarmed natural hazards due to global climate change. Such natural disasters are coastal inundation in response to sea-level rise, and/or river flooding caused by heavy rain falls, additionally earthquakes and, etc. In terms of natural hazards, one of the most sensitive and culturally significant areas in Turkey is the Hatay province in the east Mediterranean region. The Hatay province is located on such a region which is not only vulnerable to coastal inundation and river flooding, but also is a tectonically and seismically sensitive area. In this study, for taking conservation measures against the natural hazards beforehand and decision-making on any future land-planning; a digital terrain model and a 3D fly-through model of the Hatay province were generated; then quantitatively and/or qualitatively interpreted by employing the Shuttle Radar Topographic Mission digital elevation model. Besides, stream drainage patterns, lineaments and structural-geological features were extracted for natural hazard risk interpretation of settlements and their relationships among the landscape characteristics were exhibited by combining tectonic information previously confirmed. Regarding the sea-level rise, the coastal inundation risk map indicates that the most vulnerable areas are: coastlines of Iskenderun, Arsuz, Payas and Samandag, respectively. By/after analyzing the digital terrain of the study region and stream drainage patterns, the Karasu Valley Zone, where the Amik plain, settlements of Antakya, Iskenderun, Arsuz, Payas and Samandag with their flood plains have the most flooding risk in decreasing order, respectively when a heavy raining occurs. Finally, analysis of tectonics has revealed that Antakya, Iskenderun, Hassa, Kirikhan, Samandag, Payas, Arsuz, Altinozu, Kumlu and Hacipasa regions have the most sensitivity to earthquake disaster in the study region.Izmir Institute of Technolog

Flood hazard vulnerability for settlements of Turkey’s province of Edirne, using ASTER DEM data and Landsat-7 ETM+ image data

While Turkey’s province of Edirne represents one of the country’s most significant cultural heritage areas because it lies in the basins of the Meric and Ergene rivers, this very valuable region is highly susceptible to flooding during heavy rain falls. It becomes particularly vulnerable when neighboring Bulgaria responds to its own threats of heavy rain or snowfall by opening its floodgates of its dams on the River Meric, which flows through the Edirne province. Therefore, for years, the Edirne province has experienced severe floods that are eroding its fertile alluvial agricultural floodplains. An environmental plan based on a determination of the vulnerability levels of the province’s flood hazard risk areas is required if action is taken to alleviate this problem. The objective of this study is to acquire geo-information from the remotely sensed data and to interpret the flood hazard risk levels of the area’s settlements and agricultural floodplains. In this study, the spatial distribution of the flood hazard risk areas in the Edirne province is determined using not only the Advanced Space-Borne Thermal Emission and Reflection Radiometer digital elevation model data of the Edirne province to create maps that illustrate the digital terrain model and the 3D fly-through dynamic model of the study region but also the Landsat-7 Enhanced Thematic Mapper Plus multi-spectral image data set to create land use and land cover types of the study region. The maps exhibit landform characteristics, floodplain topography, and stream drainages. Analysis and interpretation of the maps demonstrate that the areas most susceptible to flooding are Enez, which lies at the northern coastal area of the Aegean Sea and agricultural areas, and the settlements on the Meric River floodplains of Ipsala, Meric, Edirne, and Uzunkopru, listed in decreasing order, respectively.Izmir Institute of Technolog