A Parallel Approach for Oil Palm Tree Detection on a SW26010 Many-Core Processor

Counting and detecting oil palm trees from high-resolution remotely sensed images is a significant work for improving economy of several countries such as Malaysia, Indonesia, etc. However, rare attention have been paid on accelerating tree crown detection algorithms on high performance platforms. In this paper, we design a parallel approach for oil palm tree detection on a SW26010 many-core processor, which is used in a world-leading supercomputer, Sunway TaihuLight. Our parallel framework contains three steps, local maximum filtering, oil palm tree crown center reassignment and oil palm tree crown center merging. Experimental results indicates that our parallel approach of oil palm tree detection obtains the speedup of 32.30 times and 1.74 times for a QuickBird image with a size of $12,188\times 12,576$ pixels compared with the well-optimized software implementation of the original algorithm on an Intel 12-core CPU and FPGAs.