A parallel approach of multi-level morphological active contour algorithm for individual tree detection and crown delineation

Forests in Taiwan contain a diverse variety. Remote sensing data could offer information of large area sampling, but it is very challenging to automatically detect tree and delineate three crown in remote sensing data. Recently a algorithm, called multi-level morphological active contour algorithm (MMAC), has been proposed to address these issues in [1]. It combines a multi-level morphological approach with the active contour model. However, this algorithm comes with a price, which is huge computational complexity, to prevent it from being implemented practically in medium- or large-scale images. This manuscript aimed to accelerate MMAC by developing a parallel processing approach using reconfigurable computing platform with field-programmable gate arrays (FPGA). The experimental evaluation indicated that the proposed architecture could provide around 40% acceleration with simple implementation in hardware.