【摘要】 赤潮是严重的海洋灾害,有效监测赤潮对于保护海洋生态环境具有重要意义。高光谱遥感具有光谱分辨率高、图谱合一等优势,适合于海洋赤潮监测。深度学习是机器学习领域的前沿,为高光谱遥感分类提供了新的思路。深度置信网络(Deep Belief Network,DBN)兼具监督分类与非监督分类的特点,通过构建DBN模型,将DBN应用于赤潮灾害遥感监测中,应用渤海机载高光谱遥感数据开展赤潮分类,以达到提取高光谱图像中赤潮水体范围的目的。通过设置对照实验,对比经典的SVM监督分类方法与ISODATA非监督分类方法,发现DBN模型在相同实验条件下具有更高的分类精度,赤潮遥感提取精度提高了3%～11%。更多还原

【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%.更多还原