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The Euclid distance based K-means clustering is among the hard classification algorithms. When dealing with deterministic remote sensing data, it is difficult to gain satisfactory classification results using K-means algorithm. The traditional K-means clustering algorithm is faced with several shortcomings such as locally converged optimization, being sensitive to initial clustering centers, etc....
A better understanding of travel demand will enable transit authorities to evaluate the services they offer, adjust marketing strategies and improve overall transit performance. In this paper, we aim to develop a method to identify the trip purpose of passenger flow who have trips to commercial district. While the same region always has the different functions, it is fairly challenging to identify...
Selecting suitable features is very crucial for achieving successful classification of land cover types. This paper presents a comparative study of three typical feature selection methods for the task of regional land cover classification using MODIS data. Comparison results have shown that Branch and Bound is the best for land cover classification with MODIS data, while ReliefF and mRMR achieve nearly...
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