基于卷积神经网络的遥感图像分类研究

Classification of remote-sensing image based on convolutional neural network

遥感图像分类是模式识别技术在遥感领域的具体应用,针对遥感图像处理中的分类问题,提出了一种基于卷积神经网络(convolutional neural networks,CNN)的遥感图像分类方法,并针对单源特征无法提供有效信息的问题,设计了一种多源多特征融合的方法,将遥感图像的光谱特征、纹理特征、空间结构特征等按空间维度以向量或矩阵的形式进行有效融合,以此训练CNN模型。实验表明,多源多特征相融合能够加快模型收敛速度,有效提高遥感图像的分类精度;与其他分类方法相比,CNN能够取得更高的分类精度,获得更优的分类效果。

The classification of remote-sensing images is a specific application of pattern recognition technology in theremote-sensing domain. In this paper, we propose a method for the classification of remote-sensing images based onconvolutional neural networks （CNN）. In addition, to address the difficulty of providing effective information regardinga single-source feature in convolutional neural networks, we propose a multi-source and multi-feature fusion method.We combine the spectral, texture, and spatial-structure features of remote-sensing images in the form of vectors ormatrices according to their spatial dimensions, and train the CNN model using these combined features. The experiment-al results show that multi-source and multi-feature fusion can effectively improve the model convergence speed andclassification accuracy, in comparison with traditional classification methods, and that the CNN method achieves higherclassification accuracy and classification effect.