Vedaldi, A. and Soatto, S. (2008) Quick Shift and Kernel Methods for Mode Seeking. In: Forsyth, D., Torr, P. and Zisserman, A., Eds., Computer Vision—ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, Vol. 5305, Springer, Berlin, 705-718.
https://doi.org/10.1007/978-3-540-88693-8_52

has been cited by the following article:

• TITLE: Object-Based Burned Area Mapping with Extreme Gradient Boosting Using Sentinel-2 Imagery

  AUTHORS: Dimitris Stavrakoudis, Ioannis Z. Gitas

  KEYWORDS: Operational Burned Area Mapping, Sentinel-2, Extreme Gradient Boosting (XGB), QuickShift Segmentation, Machine Learning

  JOURNAL NAME: Journal of Geographic Information System, Vol.15 No.1, February 14, 2023

  ABSTRACT: The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.