Fusion Markov Random Field Image Segmentation for a Time Series of Remote Sensed Images
Change detection on images of very different time instants from remote sensing databases and up-to-date satellite born or UAV born imaging is an emerging technology platform today. Since outdoor sceneries, principally observation of natural reserves, agricultural meadows and forest areas, are changing in illumination, coloring, textures and shadows time-by-time, and the resolution and geometrical properties of the imaging conditions may be also diverse, robust and semantic level algorithms should be developed for the comparison of images of the same or similar places in very different times. Earlier, a new method, fusion Markov Random Field (fMRF) method has been introduced which applied unsupervised or partly supervised clustering on a fused image series by using cross-layer similarity measure, followed by a multi-layer Markov Random Field segmentation. This paper shows the effective parametrization of the fusion MRF segmentation method for the analysis of agricultural areas of fine details and difficult subclasses.
The research reported in this paper was supported by Hungarian Scientific Research Fund (No. NKFIH OTKA K-120499 and KH-126513).
- 2.Ballesteros, R., Ortega, J.F., Hernandez, D., Moreno, M.A.: Onion biomass monitoring using uav-based rgb imaging. Precis. Agric. (2018)Google Scholar