Xing, J.; Wang, X., and Dong, J., 2019. Multi-adaptive feature extraction method of marine plant information in Internet of Things. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 495–500. Coconut Creek (Florida), ISSN 0749-0208.

In ocean monitoring and development, underwater image processing and recognition has gradually become a hot topic in ocean research. Because of the complex shooting environment, the underwater image is often seriously distorted. The absorption of water will lead to the attenuation of light in the transmission process, and the scattering of water will make the whole image appear atomization effect. According to the characteristics of underwater light propagation and underwater imaging system, the characteristics of underwater noise are analyzed. Butterworth high pass filter is used to smooth the backscatter noise of low frequency underwater image. On the basis of the above, SIFT feature points are extracted from the improved gray image, and K-nearest neighbor algorithm is used to match the feature points extracted from the standard image data set to obtain the information features of marine plants. The experimental results show that this method can obtain more feature points of marine plant information, and at the same time guarantee the accuracy of feature extraction time.