With the development of computer vision, attention mechanisms have been widely studied. Although the introduction of an attention module into a network model can help to improve classification performance on remote sensing scene images, the direct introduction of an attention module can increase the number of model parameters and amount of calculation, resulting in slower model operations. To solve this problem, we carried out the following work. First, a channel attention module and spatial attention module were constructed. The input features were enhanced through channel attention and spatial attention separately, and the features recalibrated by the attention modules were fused to obtain the features with hybrid attention. Then, to reduce the increase in parameters caused by the attention module, a group-wise hybrid attention module was constructed. The group-wise hybrid attention module divided the input features into four groups along the channel dimension, then used the hybrid attention mechanism to enhance the features in the channel and spatial dimensions for each group, then fused the features of the four groups along the channel dimension. Through the use of the group-wise hybrid attention module, the number of parameters and computational burden of the network were greatly reduced, and the running time of the network was shortened. Finally, a lightweight convolutional neural network was constructed based on the group-wise hybrid attention (LCNN-GWHA) for remote sensing scene image classification. Experiments on four open and challenging remote sensing scene datasets demonstrated that the proposed method has great advantages, in terms of classification accuracy, even with a very low number of parameters.

Generative adversarial networks (GANs) have effectively promoted the development of hyperspectral image classification technology in generating samples. Many GAN-based models for hyperspectral image classification use deconvolution to generate fake samples, which will cause chequerboard artefacts and affect classification performance. Furthermore, the training of GANs still faces the problem of mode collapse. Aiming at the above problems, we proposed a dual hybrid convolutional generative adversarial network (DHCGAN) for hyperspectral image classification. Firstly, the combination of nearest neighbour upsampling and sub-pixel convolution is employed in the generator, which avoids the overlap of convolution domain and effectively suppresses the chequerboard artefacts caused by deconvolution. Secondly, the traditional convolution and dilated convolution are fused in the discriminator, which expands the receptive field without increasing parameters and achieves more effective feature extraction. In addition, some adaptive drop blocks are embedded into the generator and discriminator to effectively alleviate the problem of mode collapse. Experiments were performed on four hyperspectral datasets (including three classical datasets – Indian Pines, University of Pavia and Houston, a new dataset – WHU-Hi-HanChuan). Experimental results show that the proposed method can provide a certain performance improvement over some competing methods, such as the accuracy has been increased by more than 1% on the three classical datasets, and even got over 3% improvement on WHU_Hi_HanChaun dataset.