Browsing by Subject "Satellites"

Now showing 1 - 4 of 4

Open Access
Estimation of 3D electron density in the Ionosphere by using fusion of GPS satellite-receiver network measurements and IRI-Plas model
(IEEE, 2013) Tuna, Tuna; Arıkan, Orhan; Arikan F.; Gulyaeva, T.
GPS systems can give a good approximation of the Slant Total Electron Content in a cylindrical path between the GPS satellite and the receiver. International Reference Ionosphere extended to Plasmasphere (IRI-Plas) model can also give an estimation of the vertical electron density profile in the ionosphere for any given location and time, in the altitude range from about 50 km to 20000 km. This information can be utilized to obtain total electron content between any given receiver and satellite locations based on the IRI-Plas model. This paper explains how the fusion of measurements obtained from a GPS satellite-receiver network can be utilized together with the IRI-Plas model in order to obtain a robust 3D electron density model of the ionosphere. © 2013 ISIF ( Intl Society of Information Fusi.
Open Access
Ionospheric total electron content estimation using IONOLAB method
(IEEE, 2007) Nayir, H.; Arıkan, F.; Erol, C. B.; Arıkan, Orhan
Ionosphere which is an important atmospheric layer for HF and satellite communications, can be investigated through Total Electron Content (TEC). Global Positioning System provides cost-effective means for TEC estimation. Regularized TEC estimation method (D-TEI) is developed to estimate high resolution, robust TEC values. The method combines measurements of GPS satellites above 10° elevation limit and estimates can be obtained with 30 s time resolution. In this paper, parameters that are used in D-TEI method such as ionospheric height, weighting function, and satellite receiver biases are studied. It is found that TEC estimation results of D-TEI method is almost independent of ionospheric height. Different weighting functions are tried and the weighting function that minimizes non-ionospheric effects is selected. By using satellite and receiver biases in the correct form consistent TEC estimation results are obtained with IGS analysis centers. In this paper, the method is improved to include phase measurements. Taking either pseudorange or phase measurements as input, high resolution, robust TEC estimates are obtained using D-TEI method.
Open Access
Learning bayesian classifiers for scene classification with a visual grammar
(IEEE, 2005-03) Aksoy, Selim; Koperski, K.; Tusk, C.; Marchisio, G.; Tilton, J. C.
A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples.
Open Access
Uydu görüntülerinde düzenli dikim alanlarının belirlenmesi
(IEEE, 2009-04) Yalnız, İsmet Zeki; Aksoy, Selim
Uydu görüntülerindeki dikim alanlarının belirlenmesi, bölütlenmesi, sınıflandırılması ve gözlemlenmesi, bu alanların ekonomik olarak daha iyi kullanım yollarının aranmasına yardımcı olmaktadır. Bir çok insan yapısı gibi, bitkiler de bir düzene göre tarlalarda veya bahçelerde dikilmektedir. Bu bildiride, görüntülerdeki düzen bilgisini kullanarak dikim alanlarını belirleyen bir yöntem önerilmiştir. Bu yöntemde, uydu görüntüsünde nokta filtresinin cevabı üzerinde pencereler gezdirilmekte ve bu pencerelerin izdüşüm vektörleri analiz edilmektedir. Daha sonra bütün pencereler için bir düzenlilik katsayısı belirlenmektedir. Bu düzenlilik katsayıları düzenli alanların daha yüksek değerler aldığı bir düzenlilik haritası çıkartmak için kullanılmaktadır. Bu düzenlilik haritası dikim alanlarının bölütlenmesi ve sınıflandırılması için kullanılabilir. Önerilen yöntem yüksek çözünürlüklü görüntülerde fındık bahçelerinin bulunmasında denenmiş ve sonuçlar tartışılmıştır. Detecting, segmenting and classifying agricultural fields in remote sensing images enable advanced planning of the land use economically. As most human structures, plants are cultivated in some order in orchards or farms. In this paper a regularity detection method is proposed for exploiting this order information. The method slides windows over the spot filter responses of satellite images and analyzes their projection vectors. A regularity coefficient is calculated for each window. These regularity coefficients are further used for creating a regularity map, where regular regions obtain higher scores. These regularity maps can later be employed for the segmentation and classification of cultivation lands. The proposed method is illustrated in the detection of hazelnut orchards in sample high resolution satellite images. ©2009 IEEE.