Classification,Fusion And Visualization Based On Multiple Types Of Ocean Data

There are many valuable applications in the field of remote sensing monitoring.Sea ice classification and phytoplankton concentration analysis are important research fields.The analysis of sea ice types is of great significance for the study of sea ice conditions,for the safety of ship navigation and for the smooth flow of maritime trade routes.One of the research contents of this subject is to select the more mature support vector machine in the supervised classification method and combine the texture features of the pixels in the image to classify.The texture features of the image are first analyzed and then classified using the support vector machine classifier.It can be seen from experiments that this improved method can well identify the annual ice,multi-year ice and seawater in the SAR sea ice image.In order to illustrate the effectiveness of the proposed method,this experiment uses two classic classification method Maximum Likelihood and Neural Networks as comparisons.By comparison,the proposed method has a high classification accuracy.The sea ice classification method and experimental results verify the effectiveness of the method and provide a new strategy for sea ice classification.Since the current sea ice classification research mainly adopts the supervised classification method and adopts the automatic clustering method,the second part of the subject uses the Gaussian mixture model to analyze the Sentinel data,and explores the feasibility of the automatic identification algorithm in the sea ice classification field.The data obtained from the experiment is analyzed in multiple directions to explore the factors affecting the classification accuracy.To this end,it provides a new perspective for the research of sea ice classification.In the study of ocean data fusion and its visualization,this paper takes the global marine chlorophyll a concentration data collected by MODIS and ScaWiFs satellites as the object,and analyzes the data hole type based on its visualization effect.For the type of holes,the least squares method,linear interpolation,nearest neighbor and bilinear difference method are adaptively used to perform two-step data fusion.With these methods,data holes are effectively reduced or even eliminated,and global chlorophyll a data coverage is improved...