Yazar "Sanli F.B." seçeneğine göre listele

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    An application of roll-invariant polarimetric features for crop classification from multi-temporal RADARSAT-2 SAR data
    (International Society for Photogrammetry and Remote Sensing, 2018) Ustuner M.; Sanli F.B.; Abdikan S.; Esetlili M.T.; Bilgin G.
    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 June, 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.
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    Comparison of crop classification methods for the sustainable agriculture management
    (Scibulcom Ltd., 2016) Ustuner M.; Esetlili M.T.; Sanli F.B.; Abdikan S.; Kurucu Y.
    Accurate and reliable information regarding crop yields and soil conditions of agricultural fields are essential for the sustainable management of agricultural areas. The increasing necessity of the food due to the high population, global climate change and rapid urbanisation, the sustainable management of the agricultural resources is becoming more crucial for countries. Remote sensing technology offers a feasible solution for gathering the cost-effective, reliable and up-to-date information about crop monitoring by using high-resolution remote sensing data. Image classification is the one of most common method to obtain information from the remotely sensed images. Despite machine learning based classifiers such as Support Vector Machines (SVM) could provide high classification accuracy, the researchers have been still working to improve the classification accuracy. Recently, the utilisation of ensemble learning approaches in remote sensing classification is the research of interest for this purpose. In this study, we implemented six different supervised classification techniques and a classifier ensemble: Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Spectral Angle Mapper, Parallelepiped, Support Vector Machines and Winnertakes- all (WTA) classification which is an ensemble based classifier. In this study, we investigated the comparative performance of the classifiers within overall and corn-class category for the study area located in Aydin, Turkey. Radial Basis Function (RBF) kernel was used here for the SVM classification. Results demonstrate that WTA classification outperformed other classification methods whilst the Parallelepiped obtained the lowest classification accuracy 13.24%. Moreover SVM gave the second highest overall classification accuracy of 89.90%.
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    Crop type classification using vegetation indices of rapideye imagery
    (International Society for Photogrammetry and Remote Sensing, 2014) Ustuner M.; Sanli F.B.; Abdikan S.; Esetlili M.T.; Kurucu Y.
    Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46% was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.
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    Evaluation of image fusion methods using PALSAR, RADARSAT-1 and SPOT images for land use/ land cover classification
    (Springer India, 2017) Sanli F.B.; Abdikan S.; Esetlili M.T.; Sunar F.
    This research aimed to explore the fusion of multispectral optical SPOT data with microwave L-band ALOS PALSAR and C-band RADARSAT-1 data for a detailed land use/cover mapping to find out the individual contributions of different wavelengths. Many fusion approaches have been implemented and analyzed for various applications using different remote sensing images. However, the fusion methods have conflict in the context of land use/cover (LULC) mapping using optical and synthetic aperture radar (SAR) images together. In this research two SAR images ALOS PALSAR and RADARSAT-1 were fused with SPOT data. Although, both SAR data were gathered in same polarization, and had same ground resolution, they differ in wavelengths. As different data fusion methods, intensity hue saturation (IHS), principal component analysis, discrete wavelet transformation, high pass frequency (HPF), and Ehlers, were performed and compared. For the quality analyses, visual interpretation was applied as a qualitative analysis, and spectral quality metrics of the fused images, such as correlation coefficient (CC) and universal image quality index (UIQI) were applied as a quantitative analysis. Furthermore, multispectral SPOT image and SAR fused images were classified with Maximum Likelihood Classification (MLC) method for the evaluation of their efficiencies. Ehlers gave the best score in the quality analysis and for the accuracy of LULC on LULC mapping of PALSAR and RADARSAT images. The results showed that the HPF method is in the second place with an increased thematic mapping accuracy. IHS had the worse results in all analyses. Overall, it is indicated that Ehlers method is a powerful technique to improve the LULC classification. © 2016, Indian Society of Remote Sensing.
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    Soil moisture estimation from radarsat -1, asar and palsar data in agricultural fields of menemen plane of western Turkey
    (International Society for Photogrammetry and Remote Sensing, 2008) Sanli F.B.; Kurucu Y.; Esetlili M.T.; Abdikan S.
    Due to the accelerating global warming, droughts which cause severe damages especially in the agriculture became a very recurrent phenomenon in all over the world. Monitoring the characteristics of soil moisture is very important in Turkey because the major impact of the global warming on our country appears to be climate changes. In this respect, drought has become a serious threat for the country where the agriculture is one of the major income sources. Therefore, monitoring of draughts has the highest priority among the other strategies. Although the sensitivity of microwaves towards the soil moisture is well understood, retrieving soil moisture with Synthetic Aperture radar (SAR) measurements still has difficulties due to the major impact of soil texture, surface roughness and vegetation cover. In this study, SAR data gathered by different sensors for the same area in closer dates were used to estimate the relative soil moisture. The relation between the ground soil moisture and the sigma nought/backscatter values of SAR images were investigated. Sigma nought values of C band HH polarized Radarsat Fine Beam image and C Band VV polarized ENVISAT (ASAR) images as well as backscatter values of an L band HH polarized ALOS (PALSAR) satellite images were used. RADARSAT, ASAR and PALSAR images were gathered on the 28th of May, 8th of June, and 10th of June in 2006 respectively for the alluvial lands of Menemen Town, Izmir. Ground soil moisture measurements taken using gravimetric methods showed a good agreement with the backscatter values of the images obtained from different types of SAR data. A comparison among the spatial distribution of retrieved soil moisture changes from SAR images was done. The correlations between the soil moisture content and backscattering of ASAR, RADARSAT-1 and PALSAR images were found 76%, 81% and 86 % respectively. Although the resolution of RADARSAT-1 fine beam image (6.25m × 6.25m) is closer to the resolution of PALSAR image (6.25m × 6.25m), PALSAR gives better correlation than RADARSAT-1 image. Although the resolution of RADARSAT-1 and PALSAR images is far more higher than that of the ASAR image (30m × 30m), the significance of the results produced is almost similar in such flat areas.