高分遥感影像双通道并行混合卷积分类方法

Dual-channel parallel hybrid convolutional neural networks based classification method for high-resolution remote sensing image

高空间分辨率遥感影像拥有丰富的空间细节信息和多光谱信息。研究表明,二维卷积神经网络适于提取空间信息,而三维卷积神经网络更适于提取光谱信息。为了更好地利用空谱信息,本文提出一种双通道并行混合卷积神经网络(DPHCNN)方法,充分联合二维与三维卷积神经网络在空谱信息提取上的优势,同时引入混合注意力机制、多尺度卷积增强空间细节特征的提取能力,实现高分影像的精准分类。试验中利用高分二号影像数据集进行验证,与当前先进的深度学习分类方法相比,本文提出的DPHCNN方法在保证分类精度高、分类效率较好的同时,能在多时相影像分类中保持最高的稳健性,在综合评价上更具优势。

High spatial resolution remote sensing images have rich spatial detail information and multi-spectral information.Previous studies have shown that two-dimensional convolutional neural networks(CNN)are suitable for extracting spatial information,while three-dimensional CNN are more suitable for extracting spectral information.In order to make better use of spatio-spectral information,this paper innovatively proposes a dual-channel parallel hybrid convolutional neural networks(DPHCNN),which fully combines the advantages of two-dimensional and three-dimensional CNN in spatio-spectral information extraction.Simultaneously,the hybrid attention mechanism and multi-scale convolution are introduced to enhance the extraction ability of spatial detail features to achieve accurate classification of high-resolution images.In the experiment,the GF-2 image dataset was used for verification.Compared with state-of-the-art deep learning classification methods,the DPHCNN method proposed in this paper not only has the highest classification accuracy and better classification efficiency but maintains the highest robustness in multi-temporal images classification,which has more advantages in comprehensive evaluation.