Vegetation segmentation based on variational level set using multi-channel local wavelet texture and color

The existing spectrum index-based methods for detecting vegetation coverage suffer from an over-dependence on spectrum. To address these issues, this paper proposes a graph cut-based variational level set segmentation algorithm that combines multi-channel local wavelet texture (MCLWT) and color. First, the prior color is generated by automatic estimation based on the mathematical morphology with a color histogram. Then, local wavelet texture features are extracted using a multi-scale and orientation Gabor wavelet transformation followed by local median and entropy filtering. Next, in addition to the energy of color, that of MCLWT is integrated into the variational level set model based on kernel density estimation. Consequently, all energies are integrated into the graph cut-based variational level set model. Finally, the proposed energy functional is made convex to obtain a global optimal solution, and a primal-dual algorithm with global relabeling is adopted to accelerate the evolution of the level sets. A comparison of the segmentation results from our proposed algorithm and other state-of-the-art algorithms showed that our algorithm effectively reduces the over-dependence on color and yields more accurate results in detecting vegetation coverage.