Remote sensing image classification method based on space spectrum capsule generative adversarial network

The invention discloses a remote sensing image classification method based on a space spectrum capsule generative adversarial network model. The method comprises the following main steps: 1, creatinga generative adversarial network model; 2, determining a sample set; 3, training a generative adversarial network model by adopting the sample set in the step 2; 4, verifying the accuracy of the model; and 5, inputting a to-be-classified hyperspectral remote sensing image into the trained generative adversarial network model to obtain a classification result. According to the method, spectral information and spatial information are fully utilized, detail features such as relative positions of samples can be accurately modeled, and the classification precision and the classification efficiencyare greatly improved. 本发明公开了一种基于空谱胶囊生成对抗网络模型的遥感图像分类方法。该方法的主要步骤为：1、创建生成对抗网络模型；2、确定样本集；3、采用步骤2的样本集训练生成对抗网络模型；4、模型准确性验证；5、向训练后的生成对抗网络模型中输入待分类高光谱遥感图像，获得分类结果。本方法充分利用了光谱信息和空间信息，能够准确建模样本相对位置等细节特征，使得分类精度以及分类效率低大大提升。