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has been cited by the following article:

• TITLE: Forest-Fire Recognition by Sparse and Collaborative Subspace Clustering

  AUTHORS: Zhen Ye, Yifu Jiang, Shihao Shi, Jiefei Yan, Lin Bai

  KEYWORDS: Forest Fires, Flame Recognition, Sparse Subspace Clustering (SSC), Collabo-rative Subspace Clustering (CSC)

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.7 No.11, November 27, 2019

  ABSTRACT: Traditional forest-fire recognition based on the characteristics of smoke, temperature and light fails to accurately detect and respond to early fires. By analyzing the characteristics of flame, the methods based on aerial image recognition have been widely used, such as RGB-based and HIS-based methods. However, these methods are susceptible to background factors causing interference and false detection. To alleviate these problems, we investigate two subspace clustering methods based on sparse and collaborative representation, respectively, to detect and locate forest fires. Firstly, subspace clustering segments flame from the whole image. Afterwards, sparse or collaborative representation is employed to represent most of the flame information in a dictionary with l1-regularization or l2-regularization term, which results in fewer reconstruction errors. Experimental results show that the proposed SSC and CSC substantially outperform the state-of-the art methods.