Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

Phase Diagram of [Amim]Cl plus Salt Aqueous Biphasic Systems and Its Application for [Amim]Cl Recovery

In this study, binodal curves and tie line data of [Amim]Cl + salt (K3PO4, K2HPO4, K2CO3) + water aqueous biphasic systems (ABS) were measured and correlated satisfactorily with the Merchuk equation and Othmer-Tobias and Bancroft equations, respectively. [Amim]Cl could be recovered from aqueous solutions using the ABS, and the recovery efficiency could reach 96.80%. The recovery efficiency was influenced by the concentrations of the salts and their Homeister series: K3PO4 > K2HPO4 > K2CO3. Our method provides a new and effective route for the recovery of hydrophilic IL using [Amim]Cl + salt + water ABS from aqueous solutions.

Study of morphology and phase diagram of the H-shaped (AC)B(CA) ternary block copolymers using self-consistent field theory

By using a combinatorial screening method based on the self-consistent field theory (SCFT) for polymer systems, the micro-phase morphologies of the H-shaped (AC)B(CA) ternary block copolymer system are studied in three-dimensional (3D) space. By systematically varying the volume fractions of the components A, B, and C, six triangle phase diagrams of this H-shaped (AC)B(CA) ternary block copolymer system with equal interaction energies among the three components are constructed from the weaker segregation regime to the strong segregation regime, In this study, thirteen 3D micro-phase morphologies for this H-shaped ternary block copolymer system are identified to be stable and seven 3D microphase morphologies are found to be metastable.