针对扫描输入纸质彩色地图噪声干扰严重,自动分割效果不理想的现状,提出了一种先去噪后分割的地图分割新方法。根据噪声性质采用维纳滤波与小波阈值萎缩相结合的去噪方法,进行基于CIE L a b 色空间的颜色聚类。与传统的彩图分割相比,该...针对扫描输入纸质彩色地图噪声干扰严重,自动分割效果不理想的现状,提出了一种先去噪后分割的地图分割新方法。根据噪声性质采用维纳滤波与小波阈值萎缩相结合的去噪方法,进行基于CIE L a b 色空间的颜色聚类。与传统的彩图分割相比,该方法对噪声具有更强的鲁棒性,处理速度快且分割清晰。展开更多
In land-use data generalization, the removal of insignificant parcel withsmall size is the most frequently used operator. Traditionally for the generalization method, thesmall parcel is assigned completely to one of i...In land-use data generalization, the removal of insignificant parcel withsmall size is the most frequently used operator. Traditionally for the generalization method, thesmall parcel is assigned completely to one of its neighbors. This study tries to improve thegeneralization by separating the insignificant parcel into parts around the weighted skeleton andassigning these parts to different neighbors. The distribution of the weighted skeleton depends onthe compatibility between the removed object and its neighbor, which considers not only topologicalrelationship but also distance relationship and semantic similarity. This process is based on theDelaunay triangulat'on model. This paper gives the detailed geometric algorithms for this operation.展开更多
Unsupervised neural networks such as the Kohonen Self-Organizing Maps （SOM） have been widely used for searching natural clusters in multidimensional and massive data. One example where the data available for analysi...Unsupervised neural networks such as the Kohonen Self-Organizing Maps （SOM） have been widely used for searching natural clusters in multidimensional and massive data. One example where the data available for analysis can be extremely large is seismic interpretation for hydrocarbon exploration. In order to assist the interpreter in identifying characteristics of interest confined in the seismic data, the authors present a set of data attributes that can be used to train a SOM in such a way that zones of interest can be automatically identified or segmented, reducing time in the interpretation process. The authors show how to associate SOM to 2D color maps to visually identify the clustering structure of the input seismic data, and apply the proposed technique to a 2D synthetic seismic dataset of salt structures.展开更多
文摘In land-use data generalization, the removal of insignificant parcel withsmall size is the most frequently used operator. Traditionally for the generalization method, thesmall parcel is assigned completely to one of its neighbors. This study tries to improve thegeneralization by separating the insignificant parcel into parts around the weighted skeleton andassigning these parts to different neighbors. The distribution of the weighted skeleton depends onthe compatibility between the removed object and its neighbor, which considers not only topologicalrelationship but also distance relationship and semantic similarity. This process is based on theDelaunay triangulat'on model. This paper gives the detailed geometric algorithms for this operation.
文摘Unsupervised neural networks such as the Kohonen Self-Organizing Maps （SOM） have been widely used for searching natural clusters in multidimensional and massive data. One example where the data available for analysis can be extremely large is seismic interpretation for hydrocarbon exploration. In order to assist the interpreter in identifying characteristics of interest confined in the seismic data, the authors present a set of data attributes that can be used to train a SOM in such a way that zones of interest can be automatically identified or segmented, reducing time in the interpretation process. The authors show how to associate SOM to 2D color maps to visually identify the clustering structure of the input seismic data, and apply the proposed technique to a 2D synthetic seismic dataset of salt structures.
