Remote sensing big data (RSBD) is generally characterized by huge volumes, diversity, and high dimensionality. Mining hidden information from RSBD for different applications imposes significant computational challenges. Clustering is an important data mining technique widely used in processing and analyzing remote sensing imagery. However, conventional clustering algorithms are designed for relatively small datasets. When applied to problems with RSBD, they are, in general, too slow or inefficient for practical use. In this paper, we proposed a parallel subsampling-based clustering (PARSUC) method for improving the performance of RSBD clustering in terms of both efficiency and accuracy. PARSUC leverages a novel subsampling-based data partitioning (SubDP) method to realize three-step parallel clustering, effectively solving the notable performance bottleneck of the existing parallel clustering algorithms; that is, they must cope with numerous repeated calculations to get a reasonable result. Furthermore, we propose a centroid filtering algorithm (CFA) to eliminate subsampling errors and to guarantee the accuracy of the clustering results. PARSUC was implemented on a Hadoop platform by using the MapReduce parallel model. Experiments conducted on massive remote sensing imageries with different sizes showed that PARSUC (1) provided much better accuracy than conventional remote sensing clustering algorithms in handling larger image data; (2) achieved notable scalability with increased computing nodes added; and (3) spent much less time than the existing parallel clustering algorithm in handling RSBD.

No abstract

With the continuous growth of the quantity, scale, and speed of vessels in recent years, maritime accidents are posing increasing risks to societies and individuals, especially in narrow inland waterways. Therefore, it is of great significance to analyze navigational risks to ensure the safety of waterborne transportation. In this paper, the navigational risks of Nanjing Yangtze River Bridge (NYRB) waters are investigated based on spatiotemporal mining on massive automatic identification system (AIS) trajectories by using geographic information system (GIS) techniques. A time-series-oriented trajectory processing method is proposed to deal with the historical AIS data in the whole year of 2019. The method adopts a periodic processing strategy to produce traffic density estimation products in multiple temporal scales for supporting spatiotemporal analysis. The proposed method greatly improves the data-processing efficiency and provides a flexible way to deeply understand the vessel behavior patterns in NYRB waters. Then the complete characteristics of the spatial distribution and temporal variation of AIS trajectories are revealed. Based on that, three types of critical navigational risks are discovered, which include the safety distance risk, the pier collision risk, and the traffic congestion risk. Moreover, we find that the greatest risk is existed in small vessels in the flood season, which is worth the most concern.

Motivated by the widespread use of real-time video streaming techniques over peer-to-peer networks, we propose a design of a peer-to-peer solution for real-time remote sensing imagery browsing. In this paper we firstly identify the characteristics of image browsing and mapping them to P2P-based streaming. Our design makes use of Tracker in locating remote sensing data resources. Further, a dynamic image tiles downloading queue technique which optimize image transmission is adopted. Considering that the conventional data piece selection algorithm of P2P systems has many limitations in supporting image streaming, our new approach gives each image tile a priority level before being transmitted. We use a parameter K to strike the balance between high priority tiles and low priority tiles. Experimental results show that the prototype system performances much better than the traditional C/S mode when an appropriate parameter K is chosen.