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Research on the Extraction of Red Tide Hyperspectral Remote Sensing Based on the Deep Belief Network (DBN)
Pages: 1-7
Year: Issue:  2
Journal: Journal of Ocean Technology

Keyword:  red tidehyperspectral remote sensingclassificationDeep Belief Network(DBN);
Abstract: Red tide is a kind of serious marine disaster. Effective monitoring on red tides is of great significance for the protection of marine ecological environment. Hyperspectral remote sensing has the advantages of high spectral resolution and combines image with spectrum, which is suitable for marine red tide monitoring. Deep learning is the frontier of machine learning, which provides a new idea for hyperspectral remote sensing classification. Deep Belief Network(DBN) has the characteristics of both supervised classification and unsupervised classification. By constructing DBN model, DBN is applied to remote sensing monitoring on red tide disasters, and the Airborne Hyperspectral Remote Sensing Data of the Bohai Sea are used to classify red tides, in order to extract the range of red tide water in hyperspectral images. Compared with the classical SVM supervised classification method and ISODATA unsupervised classification method, the DBN model has higher classification accuracy under the same experimental conditions, and the accuracy of red tide remote sensing extraction is improved by 3%-11%.
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