Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is approximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order to address these problems, a method of fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm is proposed. Firstly, in order to enhance the performance of image segmentation, the guided filtering is employed to improve neighborhood image template instead of mean filtering. Additionally, the probabilities of target and background in twodimensional histogram are exactly calculated to get more accurate threshold. Finally, the trace of the interclass dispersion matrix is taken as the fitness function of estimation of distributed algorithm, and the optimal threshold is obtained by constructing and sampling the probability model. Extensive experimental results demonstrate that our method can effectively preserve details of the target, improve the segmentation precision, and reduce the running time of algorithms.

Land desertification is a major challenge to global sustainable development. Therefore, the timely and accurate monitoring of the land desertification status can provide scientific decision support for desertification control. The existing automatic interpretation methods are affected by factors such as “same spectrum different matter”, “different spectrum same object”, staggered distribution of desertification areas, and wide ranges of ground objects. We propose an automatic interpretation method for the remote sensing of land desertification that incorporates multi-scale local binary pattern (MSLBP) and spectral features based on the above issues. First, a multi-scale convolutional LBP feature extraction network is designed to obtain the spatial texture features of remote sensing images and fuse them with spectral features to enhance the feature representation capability of the model. Then, considering the continuity of the distribution of the same kind of ground objects in local space, we designed an adaptive median filtering method to process the probability map of the extreme learning machine (ELM) classifier output to improve the classification accuracy. Four typical datasets were developed using GF-1 multispectral imagery with the Horqin Left Wing Rear Banner as the study area. Experimental results on four datasets show that the proposed method solves the problem of ill classification and omission in classifying the remote sensing images of desertification, effectively suppresses the effects of “homospectrum” and “heterospectrum”, and significantly improves the accuracy of the remote sensing interpretation of land desertification.

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To enhance the triangle quality of a reconstructed triangle mesh, a novel triangle mesh standardization method based on particle swarm optimization (PSO) is proposed. First, each vertex of the mesh and its first order vertices are fitted to a cubic curve surface by using least square method. Additionally, based on the condition that the local fitted surface is the searching region of PSO and the best average quality of the local triangles is the goal, the vertex position of the mesh is regulated. Finally, the threshold of the normal angle between the original vertex and regulated vertex is used to determine whether the vertex needs to be adjusted to preserve the detailed features of the mesh. Compared with existing methods, experimental results show that the proposed method can effectively improve the triangle quality of the mesh while preserving the geometric features and details of the original mesh.

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Aiming at the issue that mesh smoothing is hard to balance in terms of noise removing and feature preserving, in this paper, we combine the facet normal representing global geometric features of the mesh with the vertex normal characterizing local details of the mesh and propose a bi-normal mesh smoothing method based on vertex feature selection (BNBVF). Firstly, the guided filtering algorithm is extended to calculate accurately the facet normal in the field of geometric processing. The key of this portion is computing the guided normal of facet by using a geometric neighboring patch with the most consistent normal. Then, an adaptive tensor voting method is employed to divide the vertices of the mesh into feature vertices and non-feature vertices. Thirdly, a method of the neighborhood facets clustering combining with the plane fitting is proposed to calculate the normal of feature vertex, and the weighting average of first-order neighborhood facets of the vertex is applied to compute the normal of non-feature vertex. Finally, the vertices of the mesh are updated iteratively by combining the geometric information of the facet normal and vertex normal to achieve mesh smoothing. Experimental results demonstrate that the superior performance of our proposed algorithm to state-of-the-art approaches in feature preserving and error reducing. INDEX TERMS mesh smoothing, feature-preserving, bi-normal, guided filtering, tensor voting

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