Yuan, Y., Lin, J. and Wang, Q. (2016) Hyperspectral Image Classification via Multi-Task Joint Sparse Representation and Stepwise MRF Optimization. IEEE Transactions on Cybernetics, 46, 2966-2977.
https://doi.org/10.1109/TCYB.2015.2484324

has been cited by the following article:

• TITLE: Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil

  AUTHORS: Márcia Macedo, Maria Maia, Emilia Kohlman Rabbani, Oswaldo Lima Neto

  KEYWORDS: Roads, Multispectral Images, Hyperspectral Images, Optimum Path Forest Algorithm

  JOURNAL NAME: Journal of Geographic Information System, Vol.12 No.1, February 27, 2020

  ABSTRACT: One of the principal difficulties related to road safety management in Brazil is the lack of data on road projects, especially those on rural roads, which makes it difficult to use road safety studies and models from other countries as a reference. Updating road networks through the use of hyperspectral remote sensing images can be a good alternative. However, accurately recognizing and extracting hyperspectral images from roads has been recognized as a challenging task in the processing of hyperspectral data. In order to solve the aforementioned challenges, Hyperion hyperspectral images were combined with the Optimum Forest Path (OPF) algorithm for supervised classification of rural roads and the effectiveness of the OPF and SVM classifiers when applied to these areas was compared. Both classifiers produced reasonable results, however, the OPF algorithm outperformed SVM. The higher classification accuracy obtained by the OPF was mainly attributed to the ability to better distinguish between regions of exposed soil and unpaved roads.