Hyperspectral image (HSI) classification is a procedure of interest in remote sensing. HSIs contain complex spectral and spatial information, so classification tasks remain difficult. Although current deep-learning models have made significant progress in HSI classification, dealing with spectral and spatial information still requires careful investigation. To better manage spectral and spatial information and improve classification accuracy, we introduce a multiscale residual weakly dense network with an attention mechanism. First, we designed two residual weakly dense (Res-WDens) branches to extract spectral and spatial feature information and then applied the Concat method to fuse the two kinds of information. We also designed a plug-and-play hybrid attention module to refine the fused information so the network could focus on the essential spectral and spatial features. Finally, considering the relevance of spectral and spatial information, a dual-channel multiscale feature extraction module was used to extract the spectral–spatial multiscale information of HSIs. The overall accuracies of our proposed method reached 99.76%, 99.97%, and 100% on three publicly available datasets. A series of experiments demonstrated that our method is comparable to current state-of-the-art methods.
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.
Hyperspectral images (HSIs) contain a significant amount of spectral and spatial information, together with underlying redundancy and noise, causing difficulty in HSI-processing tasks. State-of-the-art deep learning methods have obtained unprecedented performance in HSI classification and analysis. However, these architectures face challenges of declining accuracy and lengthy training times. We propose a framework to mitigate these issues, composed of a densely connected spectral block and preactivation bottleneck residual spatial block to separately learn spectral and spatial features. The spectral extraction block can involve more spectral features with the increase of the network depth, and it solves the problem of lengthy training time in traditional methods, and its densely connected structure achieves higher accuracy. In the spatial extraction block, we use the improved residual structure and introduce batch normalization and a parametric rectified linear unit before convolutional layers to preactivate the network, reducing parameters, and overfitting. In experiments using three classification approaches for comparison, it can be observed that even compared to the state-of-the-art method: spectral–spatial residual network for HSI classification, the proposed model shows improvements in accuracy of 0.49%, 0.19%, and 0.35% on the Indian Pines, University of Pavia, and Kennedy Space Center datasets, respectively. The experimental results reveal that the model obtains better classification results while effectively decreasing the training time.
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