AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

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 (α) 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) Hα, (2) HαSpan, (3) HαA, (4) HαASpan 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 HαASpan (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 HαASpan while lowest classification accuracy was obtained as 66.99 % by NB and Hα. 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

Investigation on different mulch materials and chemical control for controlling weeds in apple orchard in Turkey

Five different applications of mulching, consisting of a cover of black plastic cover (polyethylene = 0.15 mm thick), a sand stratum with a thickness of 2 cm, a layer of sand with a thickness of 4 cm, cardboard (1.5 mm thick), wheat stem (5 cm thick) and glyphosate acid active substance herbicide (postemerging), respectively were used for weed control in apple orchard having different kinds of Scarlet spur, Grany smith and Red chif. 47 species of weeds belonging to 19 families were identified on the research field. Densities of weeds identified aforesaid according to application research is 0.081, 19.256, 1.243, 0.209, 26.625 and 5.799 weeds/m 2 respectively. Therefore, percent of effectiveness of aforementioned applied methods for control with weeds were determined as 99.86, 68.82, 97.98, 99.66, 56.89 and 90.61%, respectively. While the method using a cover of black plastic (polyethylene) comes first with an effectiveness of 99.86%, it was followed by cardboard method with 99.66%, sand method with 97.98% and herbicide (touchdown = glyphosate acid) method with 90.61%. © 2011 Academic Journals

Organizational Citizenship Behaviors

In this research, it is aimed to reveal the relationships among, secondary school teachers' perceptions of organizational politics, organizational dependence and organizational citizenship behaviours. 2972 teachers teaching in Malatya's central districts (Battalgazi and Yesilyurt) during 2017-2018 academic year constitute the universe of the research. 856 of whom were defined through stratified sampling method as the sample of the research. In this relational screening model based research, a model was suggested by researchers for the relationship between variables and this model was tested by SEM analyse. At the end of the research; its understood that the perceptions of organizational politics of secondary school teachers' predict their "organizational commitment and organizational citizenship behaviours significantly in a negative way", estimate "organizational commitment' and organizational citizenship behaviours significantly in a positive way" and have a partial procuration role between organizational commitment perceptions of organizational politics associated with organizational citizenship behaviour. Besides, it is comfirmed that the research explains nearly the %59 of the variance of secondary school teachers' perception of organizational politics perception and organizational commitment on organizational citizenship behaviours.C1 [Celik, Osman Tayyar] Pamukkale Univ, Denizli, Turkey.[Ustuner, Mehmet] Inonu Univ, Egitim Yonetimi ABD, Egitim Bilimleri Bolumu, Egitim Fak, Malatya, Turkey

A Telemetry Antenna System for Unmanned Air Vehicles

Abstract-This paper presents a low VSWR high gain telemetry antenna system manufactured for UAVs that provides 360 • coverage in the roll plane of the UAV. Proposed telemetry antenna system includes four telemetry antennas, one power divider that has one input and four output terminals which feeds the telemetry antennas with equal magnitude and phase. Proposed high gain telemetry antennas are based on the feeding of the microstrip patch antenna via aperture coupling. Full coverage in the roll plane of the UAV is obtained by using circular array configuration of telemetry antennas. RF power divider is designed by using couple of Wilkinson power dividers with equal line lengths and impedance sections from input terminal to the all four output terminals

Balanced vs imbalanced training data: Classifying rapideye data with support vector machines

23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016 -- 12 July 2016 through 19 July 2016 -- -- 122460The 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

BALANCED VS IMBALANCED TRAINING DATA: CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES

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

Optimization of Aperture Coupled Microstrip Patch Antennas

Abstract-Aperture coupled microstrip patch antennas (ACMPA) are special class of microstrip antennas with high gain and wide impedance bandwidth. These antennas differ from other microstrip antennas with their feeding structure of the radiating patch element. Input signal couples to the radiating patch through the aperture that exists on the ground plane of the microstrip feedline. These special antennas are multilayer stacked type of antennas with so many design variables that will affect the antenna performance. This paper presents the design and optimization procedure of ACMPA while taking care of all possible design variables and parameters to get the highest possible antenna gain and minimum VSWR

Land use and cover classification of Sentinel-IA SAR imagery: A case study of Istanbul [Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi: Istanbul Örnegi]

25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703In this study, Sentinel-1A SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and K-NN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest classification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-1A imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy. © 2017 IEEE