JAMP  Vol.7 No.11 , November 2019
Forest-Fire Recognition by Sparse and Collaborative Subspace Clustering
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.
Cite this paper: Ye, Z. , Jiang, Y. , Shi, S. , Yan, J. and Bai, L. (2019) Forest-Fire Recognition by Sparse and Collaborative Subspace Clustering. Journal of Applied Mathematics and Physics, 7, 2883-2890. doi: 10.4236/jamp.2019.711197.

[1]   Wang, Y.B. and Ma, X.M. (2014) Early Fire Detection for High Space Based on Video-Image Processing. 2014 International Symposium on Computer, Consumer and Control, Taichung, 10-12 June 2014.

[2]   Qiu, T., Yan, Y. and Lu, G. (2012) An Autoadaptive Edge-Detection Algorithm for Flame and Fire Image Processing. IEEE Transactions on Instrumentation and Measurement, 61, 1486-1493.

[3]   Wang, L. and Li, A.G. (2017) Early fire recognition based on Multi-Feature Fusion of Video Smoke. The 36th Chinese Control Conference, Dalian, 26-28 July 2017.

[4]   Chen, T.-H., Wu, P.-H. and Chiou, Y.-C. (2004) An Early Fire-Detection Method Based on Image Processing. 2004 International Conference on Image Processing, Singapore, 24-27 October 2004.

[5]   Gonzalez, R.C. and Woods, R.E. (2017) Digital Image Processing. Publishing House of Electronics In-dustry, Beijing.

[6]   Chamorro-Martines, J., Soto-Hidalgo, J.M., Martinez-Jimenez, P.M., et al. (2017) Fuzzy Color Spaces: A Conceptual Approach to Color Vision. IEEE Transaction on Fuzzy Systems, 25, 1264-1280.

[7]   Celik, T. and Demirel, H. (2009) Fire Safety in Video Sequence Using a Generic Color Model. Fire Safety, 44, 951-61.

[8]   Elhamifar, E. and Vidal, R. (2013) Sparse Subspace Clustering: Algorithm, Theory, and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 2765-2781.

[9]   Zhang, Y.B., Zhang, Y.L., Zhang, J. and Dai, Q.H. (2016) CCR: Clustering and Collaborative Representation for Fast Single Image Super-Resolution. IEEE Transactions on Multimedia, 18, 405-417.

[10]   Elhamifar, E. and Vidal, R. (2009) Sparse Subspace Clustering. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, 20-25 June 2009.

[11]   Zhang, L., Yang, M. and Feng, X.C. (2011) Sparse Representation or Collaborative Representation: Which Helps Face Recognition? IEEE International Conference on Computer Vision, Barcelona, Spain, November 2011, 471-478.