Graph Embedding via High Dimensional Model Representation for Hyperspectral Images

Learning the manifold structure of remote sensing images is of paramount relevance for modeling and understanding processes, as well as to encapsulate the high dimensionality in a reduced set of informative features for subsequent classification, regression, or unmixing. Manifold learning methods have shown excellent performance to deal with hyperspectral image (HSI) analysis but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample data. A common assumption to deal with the problem is that the transformation between the high-dimensional input space and the (typically low) latent space is linear. This is a particularly strong assumption, especially when dealing with hyperspectral images due to the well-known nonlinear nature of the data. To address this problem, a manifold learning method based on High Dimensional Model Representation (HDMR) is proposed, which enables to present a nonlinear embedding function to project out-of-sample samples into the latent space. The proposed method is compared to manifold learning methods along with its linear counterparts and achieves promising performance in terms of classification accuracy of a representative set of hyperspectral images.Comment: This is an accepted version of work to be published in the IEEE Transactions on Geoscience and Remote Sensing. 11 page

Support Vector Selection and Adaptation for Classification of Remote Sensing Images

Classification of nonlinearly separable data by nonlinear support vector machines is often a difficult task especially due to the necessity of a choosing a convenient kernel type. In this study, we propose a new classification method called support vector selection and adaptation (SVSA) that is applicable to both linearly and nonlinearly separable data in terms of some reference vectors generated by processing of support vectors obtained from the linear SVM. The method consists of two steps called selection and adaptation. In these two steps, once the support vectors are obtained by a linear SVM, some of them are rejected and others are selected and adapted to become the reference vectors. Classification is next carried out by using the K Nearest Neighbor Method (KNN) with the reference vectors. In the first step, all support vectors are classified by KNN with respect to the training data excluding the support vectors. The misclassified support vectors are rejected, and the remaining support vectors are chosen as the reference vectors. In the second step, the reference vectors are adapted by moving them towards to or away from the decision boundaries by the Learning Vector Quantization (LVQ) method. At the end of the adaptation process, the reference vectors are finalized. During classification, the class of each input vector is detected with the minimum distance rule in which the distances are calculated from the input vector to all the reference vectors. The SVSA method was experimented with some synthetic and real data, and the experimental results showed that the SVSA is competitive with the traditional SVM

Global Sensitivity Analysis of Polarimetric Data to Retrieve Biophysical Parameters of Canola and Barley Crops

Tracking crop’s biophysical parameters using temporal Pol-SAR (Polarimetric Synthetic Aperture Radar Data) data is an active research topic in precision agriculture due to the sensitivity of PolSAR acquisition to canopy’s physical and geometrical structure. Reconstruction of polarimetric features from collection of SAR data is computationally expensive, and more important, the inter-features correlations cause decreased performance in regression based biophysical parameter estimation. With the scope of operational crop monitoring, this study provides key variables to drive Leaf Area Index (LAI) from polarimetric data based on global sensitivity analysis (GSA) addressing the ranking of the most influential features. We applied variance-based GSA for temporal fully polarimetric RadarSAT-2 images acquired through the cultivation period of two crops; canola and barley. Among 20 polarimetric features, anisotropy and correlation magnitude between co-polar channels were found to be the most influential polarimetric features for canola and barley, respectively

The effects of marble wastes on soil properties and hazelnut yield

WOS: 000341348600015Wastes generated in the dimension stone industry have become an environmental concern in Turkey which is one of the leading dimension stone (mostly marble and travertine) producers in the world. Use of such wastes rich in CaCO3 for the remediation of acidic and calcium deficient soils might be an environmentally sound way to reduce the amount of wastes to be disposed. The objective of this study was to determine the effects of marble quarry and cutting wastes on the soil properties and Tombul hazelnut cultivar under the field conditions. Field tests were conducted for one year in Giresun, Turkey. The results showed that marble wastes had a significant effect on the neutralization of the soil as well as on the hazelnut yield. The soil pH was increased from 4.71 to 5.88 upon marble waste application at rates equal to agricultural lime requirement. Hazelnut yield increased from 1120.3 kg ha(-1) on the field with no marble waste treatment to 1605.5 kg ha(-1) with marble wastes. This study indicates that marble quarry and cutting wastes could be used in the hazelnut fields for the neutralization of acidic soil to increase the yield. (C) 2014 Elsevier Ltd. All rights reserved.Ataturk University BAP projectAtaturk University [2012/187]The authors gratefully acknowledge the financial support by Ataturk University BAP project (2012/187) through grant number 2012/187

Hydrophobic Pesticide Endosulfan (alpha plus beta) and Endrin Sorption on Different Types of Microplastics

Ease of plastics production has caused high usage in many areas from water bottles, furniture and packaging to cosmetics during the last decade. Plastic products cause environmental pollution due to the high demand in the industry. It is known that pesticides are used to prevent various pests in order to increase production efficiency. With the use of such substances, there is considerable growth in the amount of product. However, pesticides remain on the water, soil, fruits, and vegetables for a long time, causing environmental pollution and thus leading to various damages that can reach people through the food chain. Since some of the mostly found pesticides in the environment are endosulfan and endrin, the sorbing capacities of endosulfan and endrin on microplastics have been investigated. The kinetic parameters at 23 degrees C in ultrapure water, hexane and saline water have been tested to show the effect of solvents on sorption behaviors