<abstract> <p>Hyperspectral images contain abundant spectral and spatial information of the surface of the earth, but there are more difficulties in processing, analyzing, and sample-labeling these hyperspectral images. In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression model are introduced to propose a sample labeling method based on neighborhood information and priority classifier discrimination. A new hyperspectral remote sensing image classification method based on texture features and semi-supervised learning is implemented. The LBP is employed to extract features of spatial texture information from remote sensing images and enrich the feature information of samples. The multivariate logistic regression model is used to select the unlabeled samples with the largest amount of information, and the unlabeled samples with neighborhood information and priority classifier discrimination are selected to obtain the pseudo-labeled samples after learning. By making full use of the advantages of sparse representation and mixed logistic regression model, a new classification method based on semi-supervised learning is proposed to effectively achieve accurate classification of hyperspectral images. The data of Indian Pines, Salinas scene and Pavia University are selected to verify the validity of the proposed method. The experiment results have demonstrated that the proposed classification method is able to gain a higher classification accuracy, a stronger timeliness, and the generalization ability.</p> </abstract>

To make full use of geodetic height results measured by GNSS and improve the accuracy that GNSS geodetic height convert to normal height, method of polynomial surface fitting has been selected in this article to research into fitting of the elevation. In the first place, for least squares estimation do not have the ability of resisting gross error, robust estimation is introduced to data preprocessing, which has solve the problem of distortion model effectively and then combines with specific engineering to make comparison and to analyze accuracy of polynomial surface fitting data of different orders. MATLAB has been used in programming design in the whole process, which has realized automatic processing of data.

Abstract. Hyperspectral images contain abundant spectral and spatial information about the earth's surface, labeling data processing and analysis more difficult, as well as the problem of sample labeling. In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression model are introduced, and a sample labeling method based on neighborhood information and priority classifier discrimination is presented. Then, a hyperspectral remote sensing image classification method based on texture features and semi-supervised learning is implemented. The LBP is employed to extract features of spatial texture information from remote sensing images and enrich the feature information of samples. Then the multivariate logistic regression model is used to select the unlabeled samples with the largest amount of information, and the unlabeled samples with neighborhood information and priority classifier tags are selected to obtain the pseudo-labeled samples after learning. By making full use of the advantages of sparse representation and mixed logistic regression model, a new hyperspectral remote sensing image classification model based on semi-supervised learning is constructed to effectively achieve accurate classification of hyperspectral images. The data of Indian Pines, Salinas scene and Pavia University are selected to verify the validity of the proposed method. The experiment results show that the proposed classification method can obtain higher classification accuracy and show stronger timeliness and generalization ability.

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