Li Pingxiang

A developed algorithm of apriori based on association analysis

A method for mining frequent itemsets by evaluating their probability of supports based on association analysis is presented. This paper obtains the probability of every 1-itemset by scanning the database, then evaluates the probability of every 2-itemset, every 3-itemset, everyk-itemset from the frequent 1-itemsets and gains all the candidate frequent itemsets. This paper also scans the database for verifying the support of the candidate frequent itemsets. Last, the frequent itemsets are mined. The method reduces a lot of time of scanning database and shortens the computation time of the algorithm.

A sub-pixel mapping algorithm based on artificial immune systems for remote sensing imagery

In this paper, a new sub-pixel mapping method inspired by the clonal selection algorithm (CSA) in artificial immune systems (AIS) is proposed, namely clonal selection subpixel mapping (CSSM). In CSSM, the sub-pixel mapping problem becomes one of assigning land cover classes to the sub-pixels while maximizing the spatial dependence by clonal selection algorithm. CSSM inherits the biologic properties of human immune systems, i.e. clone, mutation, memory, to build a memory-cell population with a diverse set of local optimal solutions. Based on the memory-cell population, CSSM outputs the value of the memory cell and find the optimal sub-pixel mapping result. The proposed method was tested using the synthetic and degraded real imagery. Experimental results demonstrate that the proposed approach outperform traditioanl sub-pixel mapping algorithms, and hence provide an effective option for sub-pixel mapping of remote sensing imagery.

SAR image classification based on its texture features

SAR images not only have the characteristics of all-ay, all-eather, but also provide object information which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The authors conducted the experiments of texture statistics analysis on SAR image features in order to improve the accuracy of SAR image interpretation. It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpretation.

Unsupervised remote sensing image classification using an artificial DNA computing

In this paper, a spectral encoding and matching algorithm inspired by artificial DNA computing (ADC) is proposed to perform the task of unsupervised classification for hyperspectral remote sensing data. As a novel branch of biological computational intelligence, ADC has strong capabilities of pattern recognition, huge information memory, parallel and fast computation. Unsupervised classification for hyperspectral data is complicated pattern recognition problem with massive volume data. In this paper, unsupervised hyperspectral data classification task by ADC is attempted and the preliminary results are provided. The experiment was performed to evaluate the performance of the proposed algorithm compared with two known algorithms: K-means and ISODATA. It is demonstrated that our method is superior to the traditional algorithms and its overall accuracy and Kappa coefficient reach 80.96% and 0.7631 respectively.

DCAD： a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.

Linear Features Extraction From Remote Sensing Image Based on Wedgelet Decomposition

Linear feature extraction is an important problem for remote sensing image processing, and it is very difficult to extract those linear features embedded in strong noise or when the SNR (signal to noise) is low like the complicated environment of remote sensing image. In this paper, an algorithm based on wedgelet decomposition is proposed to extract linear features from remote sensing image. Firstly, beamlets can be generated by recursive dyadic partitioning, vertex marking and connecting in different scales, and beamlet transform is implemented as one important parameter to generate edge map of linear feature. Secondly, each dyadic square is split into two wedgelet segments, and wedgelet decomposition is implemented as the other important parameter to generate edge map of linear feature. The propose method can detect lines with any orientation, location and length in different scales. Experimental results show that the proposed method can extract linear features accurately from remote sensing image. It can be suited to remote sensing image processing and in practice it has surprisingly powerful and apparently unprecedented capabilities.

An Approach For Edge Detection Based On Beamlet Transform

Edge detection is very useful and important for image processing and computer vision, as it can locate significant variations of gray images. In this paper, an algorithm based on beamlet transform is proposed to detect edges in image. Beamlets can be generated by recursive dyadic partitioning, vertex marking and connecting, the beamlet transform is the collection of all line integrals formed by viewing the image as a piecewise constant object and integrating along line segment in the beamlet dictionary, for the maximal beamlet coefficient surviving the Canny criterion, draw a line segment depicting that beamlet, all these beamlets in different scales are fused to generate an edge map at the image pixel level. The propose method can detect lines with any orientation, location and length in different scales and avoids subjective setting. Experimental results show that the proposed method can detect edges accurately even from noise image and has a better performance. It can be suited to different images processing, in practice it has surprisingly powerful and apparently unprecedented capabilities.

Subspace clustering based on decision fusion strategy for hyperspectral imagery

In this paper, a novel hyperspectral subspace clustering algorithm based on decision fusion strategy (SCDFS) is proposed. Because the different clusters are contained in different subspace of the same hyper-dimensional data, the clustering processing in different subspace is conducted by genetic K-means algorithm (KGA). The clustering results from different subspace can be combined into decision string. The proposed subspace clustering based on decision fusion strategy is conducted on decision string. Considering the selection of subspace, the decision results may be inaccurate. So by the majority voting processing for different subspace, the steady subspace combination can be determined. Finally, the weighted strategy is introduced into SCDFS algorithm to evaluate the distance of different decision string, and determine the fusion clustering result.

A target detection algorithm for urban areas using HSRH imagery

This paper presents an target detection algorithm focusing on making full use of both spatial and spectral features of the high spatial resolution hyperspectral (HSRH) imagery. It use the spatial relationship between pixels in the finite impulse filter with the low dimension data transferred from the original imagery. Experiments show it performs better than the method solely depending on spectral features.

Acquisition of Directional Parameters in Aerial Images Based on DEM Data

This paper develops a method which can be used to assist aerial navigation by determining the spatial position and posture of the aerial photographic plane. After the method, aerial images match known DEM to capture the spatial position and posture. Some aerial images and terrain data are used to testify our method. Compared with those of analytic and stereo mappers, the results by our method are correspondent to real measurements well.