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.

The single-moored light buoys employed in the lower reaches of the Yangtze River play an important role in indicating ship navigation and ensuring safety. To clarify the interaction between waves and floating buoys moored to the riverbed, this paper applies a numerical approach to investigate the wave-induced motion performance of a light buoy and reveal the effects of different mooring configurations to extend its service life. An open-source smoothed particle hydrodynamics (SPH)-based numerical model named dualsphysics coupled with MoorDyn is implemented. This coupled model is validated by simulating the motion of a moored rectangle buoy in regular waves, and compared with experimental data and the numerical results of reef3d code, a mesh-based computational fluid dynamics (CFD) model. The validation results show that the coupled model reproduces experimental data well and has a smaller deviation in comparison with reef3d. Then, the coupling model is applied to simulate the hydrodynamic performance of the real-size light buoy employed in Yangtze River and investigate the effects of encounter angle between wave propagation direction and mooring chain. The results demonstrate the capability of this coupled mooring model to simulate the motion of a moored buoy in regular waves, and this numerical approach will be extended to simulate the light buoy in more complex environments such as irregular waves, flow or extreme weather in further work.