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

Extremely skewed X-chromosome inactivation patterns in women with recurrent spontaneous abortion

Background: The role of extremely skewed X-chromosome inactivation (XCI) has been questioned in the pathogenesis of recurrent spontaneous abortion (RSA) but the results obtained were conflicting. Aims: We therefore investigated the XCI patterns in peripheral blood DNA obtained from 80 patients who had RSA and 160 age-matched controls. Methods: Pregnancy history, age, karyotype, and disease information was collected from all subjects. The methylation status of a highly polymorphic cytosine-adenine-guanine repeat in the androgen-receptor (AR) gene was determined by use of methylation-sensitive restriction enzyme HpaII and polymerase chain reaction. Results: Skewed XCI (> 8 5% skewing) was observed in 13 of the 62 patients informative for the AR polymorphism (20.9%), and eight of the 124 informative controls (6.4%) (P = 0.0069; χ 2 test). More importantly, extremely skewed XCI, defined as > 90% inactivation of one allele, was present in 11 (17.7%) patients, and in only two controls (P = 0.0002; χ 2 test). Conclusions: These results support the interpretation that disturbances in XCI mosaicism may be involved in the pathogenesis of RSA. © 2006 The Authors Journal compilation © 2006 The Royal Australian and New Zealand College of Obstetricians and Gynaecologists

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

Crop Type Classification Using Vegetation Indices of RapidEye Imagery

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

FUSION OF TERRASAR-X AND RAPIDEYE DATA: A QUALITY ANALYSIS

ISPRS Conference on Serving Society with Geoinformatics (SSG) -- NOV 11-17, 2013 -- Antalya, TURKEYWOS: 000358223000006This research compares and evaluates image fusion algorithms to achieve spatially improved images while preserving the spectral information. In order to compare the performance of fusion techniques both active and passive images were used. As an active image a high resolution, X-band, VV polarized TerraSAR-X data and as a multispectral image RapidEye data were used. RapidEye provides five optical bands in the 400-850 nm range and it is the first space-borne sensor which operationally gathers the red edge spectrum (690-730 nm) besides the standard channels of multi-spectral satellite sensors. The selected study area is in the low lands of Menemen (Izmir) Plain on the west of Gediz Basin covering both agricultural fields and residential areas. For the quality analysis, Adjustable SAR-MS Fusion (ASMF), Ehlers fusion and High Pass Filtering (HPF) approaches were investigated. In this study preliminary results of selected image fusion methods were given. The quality of the fused images was assessed with qualitative and quantitative analyses. For the qualitative analysis visual comparison was applied using different band combinations of fused image and original multispectral Rapid-Eye image. In the merged images color distortions regarding to SAR-optical synergy were investigated. Statistical analysis was carried out as quantitative analyses. In this respect Correlation Coefficient (CC), Standard Deviation Difference (SDD), Universal Image Quality Index (UIQI) and Root Mean Square Error (RMSE) were performed for quality assessments. In general HPF was performed best while ASMF was performed the worst in all results.Int Soc Photogrammetry & Remote Sensin

COMPARISON OF CROP CLASSIFICATION METHODS FOR THE SUSTAINABLE AGRICULTURE MANAGEMENT

WOS: 000381331000026Accurate 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 Winner takes-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%

COMPARISON OF CROP CLASSIFICATION METHODS FOR THE SUSTAINABLE AGRICULTURE MANAGEMENT

WOS: 000381331000026Accurate 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 Winner takes-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%