Distributed parallel optimization of hyperspectral image classification based on spatial correlation regularized sparse representation

The hyperspectral image features wide coverage, high dimensional bands and a huge amount of data, which leads to time-consuming computation when processing hyperspectral data. Spark is a distributed big data processing framework, integrated in-memory computation. So Spark is suitable for complex iterative calculation. In order to classify massive hyperspectral data efficiently, the Spark version of the original Spatial Correlation Regularized Sparse Representation Classification (SCSRC) is proposed in this paper. In Distributed Parallel SCSRC (DP-SCSRC), firstly, adjacent hyperspectral image indexes are stored in the same partition of Spark's RDDs to preserve spatial correlation. Secondly, Joint Distributed Matrix (JDM) is created to reduce overhead data synchronization between computing nodes. Experimental results on real hyperspectral data demonstrate that DP-SCSRC achieves a remarkable speedup and is scalable with larger data size.