Abstract
Wildfire may cause serious damage to the forest. Because of the complexity of the forest environment video images obtained by CCD cameras of the automatic forest fire surveillance system often contain much noise. It will be one of the most troublesome things for the follow-up image processing procedures. After features of noise in the forest fire images are analyzed, a kind of forest fire image de-noising method based on the wavelet transform theory is introduced and its feasibility to remove noise in forest fire images is discussed in detail. Then several forest fire image de-noising experiments with various threshold decision strategies under the MATALB platform are carried out. At last these experiment results are compared according to SNR and image loss degree and it is showed that the wavelet de-noising method with the Bayes threshold estimation algorithm is one of the most efficient de-noising techniques for the image preprocessing procedure of a forest wildfire.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
