A. Salimi; M. Ziaii; A. Amiri; M. Hosseinjani Zadeh
Abstract
Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed ...
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Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called training samples and end-members. The performance of algorithms using training samples can be negatively affected by the curse of dimensionality. This problem is usually observed in the hyperspectral image classification, especially when a low number of training samples, compared to the large number of spectral bands of hyperspectral data, are available. An unmixing method that is not highly impressed by the curse of dimensionality is a promising option. Among all the methods used, Support Vector Machine (SVM) is a more robust algorithm used to overcome this problem. In this work, our aim is to evaluate the capability of a regression mode of SVM, namely Support Vector Regression (SVR), for the sub-pixel classification of alteration zones. As a case study, the Hyperion data for the Sarcheshmeh, Darrehzar, and Sereidun districts is used. The main classification steps rely on 20 field samples taken from the Darrehzar area divided into 12 and 8 samples for training and validation, respectively. The accuracy of the sub-pixel maps obtained demonstrate that SVR can be successfully applied in the curse of dimensional conditions, where the size of the training samples (12) is very low compared to the number of spectral bands (165).
H. Fattahi; N. Babanouri
Abstract
The tensile strength (TS) of rocks is an important parameter in the design of a variety of engineering structures such as the surface and underground mines, dam foundations, types of tunnels and excavations, and oil wells. In addition, the physical properties of a rock are intrinsic characteristics, ...
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The tensile strength (TS) of rocks is an important parameter in the design of a variety of engineering structures such as the surface and underground mines, dam foundations, types of tunnels and excavations, and oil wells. In addition, the physical properties of a rock are intrinsic characteristics, which influence its mechanical behavior at a fundamental level. In this paper, a new approach combining the support vector regression (SVR) with a cultural algorithm (CA) is presented in order to predict TS of rocks from their physical properties. CA is used to determine the optimal value of the SVR controlling the parameters. A dataset including 29 data points was used in this study, in which 20 data points (70%) were considered for constructing the model and the remaining ones (9 data points) were used to evaluate the degree of accuracy and robustness. The results obtained show that the SVR optimized by the CA model can be successfully used to predict TS.
