基于卷积神经网络的高分遥感影像内陆盐沼湿地信息提取

Inland salt marsh wetlands information extraction from high-resolution remote sensing image based on convolution neural network

本文以宁夏盐池Landsat 8影像、高分二号影像、LiDAR数据插值生成的DEM数据、地理国情普查数据等为数据源,首先利用一年多期的Landsat 8影像确定提取内陆盐沼湿地的最佳时相;然后对最佳时相的高分二号(GF-2)融合影像等数据进行多尺度叠置分割,获取NDVI、DEM、穗帽变换等特征,采用最邻近分类器提取内陆盐沼湿地信息,构建内陆盐沼湿地样本库;最后在此基础上探讨卷积神经网络用于高分卫星影像提取内陆盐沼湿地方法。试验结果表明,设计的卷积神经方法适用于内陆盐沼湿地提取,与最近邻分类法提取的结果相比,内陆盐沼湿地边界的提取效果有明显提高。

Based on Landsat 8 image,GF-2 image,DEM data generated by LiDAR data interpolation and census data of geographical conditions in Yanchi,Ningxia,in this paper,the best time phase of inland salt marsh wetland extraction is determined by using Landsat 8 image.Then the best time phase of GF-2 fusion image is segmented by multi-scale overlay,and NDVI,DEM,Tasseled Transformation and other features are selected.The nearest neighbor classifier is used to obtain the information of inland salt marshes,and the inland salt marshes sample database is constructed.On this basis,the convolution neural network method for extracting inland salt marshes from high-resolution satellite images is discussed.The experimental results show that the convolution neural method is suitable for inland salt marsh wetland extraction.Ccompared with the nearest neighbor classification method,the extraction effect of inland salt marsh wetland is significantly improved.