Recognition And Extraction Of Scattered Enteromorpha Images

As we all know,China has rich marine resources,which not only can meet people's basic needs for marine products,but also provide strong support and guarantee for the development of industry and tourism.At the same time,it is an important part of China's logistics and transportation system,plays an important role,plays an irreplaceable role,and is China's unique advantage in natural resources.However,it should be noted that in recent years,there have been many large-scale Enteromorpha disasters in the ocean,each of which has a relatively long duration and a large scale,causing serious economic losses and endangering the coastal environment of Qingdao and other coastal cities.Because Enteromorpha needs a lot of human and material resources in governance,and the cost of governance is very high,so how to find and manage as early as possible and reduce economic loss has become an important research topic.so this paper introduces the image threshold segmentation technology.Remote sensing technology has become an important monitoring method for Enteromorpha disaster because of its low cost and wide monitoring range.Based on sensing technology,this paper studies the information recognition of Enteromorpha in the image.By selecting different recognition methods,it is found that the dual band ratio method and the enhanced vegetation index method are better in the recognition of Enteromorpha image,and the purpose of identifying Enteromorpha image information is achieved.The recognition effect of single-band threshold method is relatively poor,and the enhanced vegetation index method compared with the normalized difference vegetation index method achieved the effect of enhancing vegetation information.At present,there are relatively few researches on the combination of various methods in the monitoring of Enteromorpha disaster,because of the large amount of remote sensing image date,there are most irrelevant background areas in the remote sensing image,and the threshold segmentation technology can extract the features of the image,so this paper introduces the image threshold segmentation technology.Through the selection of different threshold segmentation algorithms and segmentation thresholds,the maximum entropy threshold segmentation method has a better threshold segmentation effect,and the adaptive threshold segmentation algorithm can complete the edge detection of Enteromorpha image.Although the Otsu method and the iterative method can realize the automatic return,and improve the efficiency of the selection of the threshold value,however,there is a problem of dividing the false information of Enteromorpha into irrelevant background areas in the image.The recognition of Enteromorpha information in the image completes the qualitative analysis of Enteromorpha information,and lacks the quantitative analysis of it.Based on this,the supervised classification method is used to classify the image.By selecting different classifiers to classify the water body and Enteromorpha pixels in the image,it is found that the maximum likelihood ratio classifier has the best classification effect and improves the quantitative analysis of Enteromorpha image information.