@article{Shadaydeh17_WMF,
    type = {article},
    key = {Shadaydeh17_WMF},
    title = {Wetland mapping by fusion of airborne laser scanning and multi-temporal multispectral satellite imagery},
    author = {Maha Shadaydeh and Andras Zlinszky and Andrea Manno-Kovacs and Tamas Sziranyi},
    journal = {International Journal of Remote Sensing},
    year = {2017},
    number = {23},
    pages = {7422-7440},
    volume = {38},
    abstract = {Wetlands play a major role in Europe’s biodiversity. Despite their importance, wetlands are suffering from constant degradation and loss, therefore, they require constant monitoring. This article presents an automatic method for the mapping and monitoring of wetlands based on the fused processing of laser scans and multispectral satellite imagery, with validations and evaluations performed over an area of Lake Balaton in Hungary. Markov Random Field models have already been shown to successfully integrate various image properties in several remote sensing applications. In this article, we propose the multi-layer fusion Markov Random Field model for classifying wetland areas, built into an automatic classification process that combines multi-temporal multispectral images with a wetland classification reference map derived from airborne laser scanning (ALS) data acquired in an earlier year. Using an ALS-based wetland classification map that relied on a limited amount of ground truthing proved to improve the discrimination of land-cover classes with similar spectral characteristics. Based on the produced classifications, we also present an unsupervised method to track temporal changes of wetland areas by comparing the class labellings of different time layers. During the evaluations, the classification model is validated against manually interpreted independent aerial orthoimages. The results show that the proposed fusion model performs better than solely image-based processing, producing a non-supervised/semi-supervised wetland classification accuracy of 81-93 \% observed over different years. },
    doi = {10.1080/01431161.2017.1375614},
    eprint = {http://dx.doi.org/10.1080/01431161.2017.1375614},
    publisher = {Taylor \& Francis},
    url = {http://dx.doi.org/10.1080/01431161.2017.1375614},
}