Hyperspectral images (HSIs) contain spatial features and rich spectral features that provide them with great advantages in target classification and make it easy to improve image classification accuracy. Convolutional neural networks (CNNs) have shown good performance in HSI classification. However, blindly increasing the depth of the CNNs may lead to overfitting. A HSI classification method based on a dense multi-scale residual network is proposed to address these two problems. The proposed framework obtains the spectral-spatial characteristics of HSIs through an improved multi-scale residual network. Then, three cascaded multi-scale residual modules form a deep network. The dense connection module is used to stack feature maps from all previous layers to form the concatenate feature map rather than fusing pixels of these feature maps and further achieve the purpose of improving classification accuracy and reducing the time consumption of the network. A series of experiments show that the proposed method achieves good experimental results on three widely used hyperspectral datasets and a new hyperspectral dataset (Farmland distribution dataset, FDD).
Hyperspectral images (HSIs) contain rich spectral information and spatial information. How to apply these two information types and fully combine the correlation between them remains a challenge worthy of further research and discussion. In this study, a multi-branch-multi-scale residual fusion network (MB-MS-RFN) for HSI classification is proposed. First, a 3D multi-branch-multi-scale convolution residual network, which can acquire image features of different scale in the training process and consider the correlation between spectral information and spatial information, is developed. Instead of deepening the network, the multi-branch structure widens the network horizontally to obtain more accurate classification. Finally, the different levels of HSI features are fused to obtain better classification results. Several experiments have been carried out to verify the proposed framework, and the results have demonstrated that the proposed MB-MS-RFN framework can improve the classification performance of HSIs. The performance of the MB-MS-RFN was evaluated using the Indian Pines, Pavia University, and Kennedy Space Center datasets; the performances’ overall accuracies were 99.66%, 99.92%, and 99.97%, respectively. The results from a series of experiments confirm that the proposed method offers several advantages in classification accuracy compared with five other methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.