高光谱遥感图像(HRSI)具有相同光谱的异物特征。由于难以手动标记样品,高光谱遥感图像被理解为典型的“小样本”数据集。深度神经网络可以有效地从HRSI中提取深层特征,但分类精度主要取决于训练标签样本。因此,本文采用了堆叠式卷积自动编码器网络和传输学习策略,以设计一种新的堆叠式卷积自动编码器网络模型传输(SCAE-MT),用于对HRSI进行分类。在提出的分类方法中,为了有效地从HRSI中提取深度特征,采用堆叠的卷积au-to-编码网络。然后,应用迁移学习策略,设计了一个小样本和有限训练样本下的堆叠式卷积自动编码器网络模型迁移。为了解决HRSI的小样本问题,利用SCAE-MT模型提出了一种新的HRSI分类方法。在这项研究中,为了证明所提出的分类方法的有效性,选择了两个HRSI数据集。为了验证方法的有效性,卷积自编码网络分类方法(CAE)的总体分类精度(OA),堆栈卷积自编码网络分类方法(SCAE),SCAE-MT方法在5%以下,10%,并计算15%的训练集。与5%的CAE和SCAE模型相比,10%,和15%的训练数据集,SCAE-MT法的总体准确度(OA)提高了2.71%,3.33%,和3.07%(平均),分别。SCAE-MT方法是,因此,明显优于其他方法,也显示出良好的分类性能。
Hyperspectral remote sensing images (HRSI) have the characteristics of foreign objects with the same spectrum. As it is difficult to label samples manually, the hyperspectral remote sensing images are understood to be typical \"small sample\" datasets. Deep neural networks can effectively extract the deep features from the HRSI, but the classification accuracy mainly depends on the training label samples. Therefore, the stacked convolutional autoencoder network and transfer learning strategy are employed in order to design a new stacked convolutional autoencoder network model transfer (SCAE-MT) for the purposes of classifying the HRSI in this paper. In the proposed classification method, the stacked convolutional au-to-encoding network is employed in order to effectively extract the deep features from the HRSI. Then, the transfer learning strategy is applied to design a stacked convolutional autoencoder network model transfer under the small and limited training samples. The SCAE-MT model is used to propose a new HRSI classification method in order to solve the small samples of the HRSI. In this study, in order to prove the effectiveness of the proposed classification method, two HRSI datasets were chosen. In order to verify the effectiveness of the methods, the overall classification accuracy (OA) of the convolutional self-coding network classification method (CAE), the stack convolutional self-coding network classification method (SCAE), and the SCAE-MT method under 5%, 10%, and 15% training sets are calculated. When compared with the CAE and SCAE models in 5%, 10%, and 15% training datasets, the overall accuracy (OA) of the SCAE-MT method was improved by 2.71%, 3.33%, and 3.07% (on average), respectively. The SCAE-MT method is, thus, clearly superior to the other methods and also shows a good classification performance.