Clustering of Remote Sensing Data Based on K-Nearest Neighbors Sampling With Non-Evenly Division

Data computation and traffic is the key step to rapid analysis and intelligent transportation application based on remote sensing data. To tackle the low computing efficiency and high storage cost in the analysis of remote sensing and improve the computational performance quickly, this paper proposed a new processing method of remote sensing data based on k-nearest neighbors (KNN) sampling with non-evenly division. In the method, we first sort and preprocess the original dataset in terms of any size of one-dimension and segment the sample dataset by non-evenly division. Then the samples with the range of boundary width are reserved, and a new local unsampled mapping table is reconstructed. Next, we traverse the subset and compute the distance matrix by Euclidean distance and the local density with descending order, and further determine whether the sample belongs to boundary sample in accordance with distance matrix and local density. We then construct the sampling dataset and combine again and achieve the processing result via adding the entire unsampled mapping table to the sample dataset. Finally, the current study is tested and verified by the simulation data and true traffic jam prediction case. Our experiments present that the proposed method not only can record precisely the correspondence relations between samples and unsampled data by the KNN sampling with non-evenly division and ensure the accuracy of clustering results, but also significantly reduce the data traffic and effectively improve the memory utilization. The result reveals that the proposed method can potentially contribute to the data analysis of remote sensing data and prediction of traffic jam with large scale and high real-time performance.