12 January 2018 A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images
Author Affiliations +
Abstract
In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Likun Liu, Likun Liu, } "A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images", Proc. SPIE 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 106201U (12 January 2018); doi: 10.1117/12.2300543; https://doi.org/10.1117/12.2300543
PROCEEDINGS
13 PAGES


SHARE
Back to Top