The main challenge of scene classification is to understand the semantic context information of high-resolution remote sensing images. Although vision transformer (ViT)-based methods have been explored to boost the long-range dependencies of high-resolution remote sensing images, the connectivity between neighboring windows is still limited. Meanwhile, ViT-based methods commonly contain a large number of parameters, resulting in a huge computational consumption. In this paper, a novel lightweight dual-branch swin transformer (LDBST) method for remote sensing scene classification is proposed, and the discriminative ability of scene features is increased through combining a ViT branch and convolutional neural network (CNN) branch. First, based on the hierarchical swin transformer model, LDBST divides the input features of each stage into two parts, which are then separately fed into the two branches. For the ViT branch, a dual multilayer perceptron structure with a depthwise convolutional layer, termed Conv-MLP, is integrated into the branch to boost the connections with neighboring windows. Then, a simple-structured CNN branch with maximum pooling preserves the strong features of the scene feature map. Specifically, the CNN branch lightens the LDBST, by avoiding complex multi-head attention and multilayer perceptron computations. To obtain better feature representation, LDBST was pretrained on the large-scale remote scene classification images of the MLRSN and RSD46-WHU datasets. These two pretrained weights were fine-tuned on target scene classification datasets. The experimental results showed that the proposed LDBST method was more effective than some other advanced remote sensing scene classification methods.

Crowd counting in congested urban transport systems is a highly challenging task for computer vision and deep learning due to several factors such as mutual occlusion, perspective change, and large-scale variations. In this paper, a novel perspective-embedded scale-selection multi-column network named PESSNet is proposed for crowd counting and high-quality density maps generation. The proposed method aligns the branches to various scales by leveraging different receptive fields, and utilizes perspective parameters to adjust the sensitivity of each branch to different perspective areas in the scene. Specifically, the PESSNet consists of four key components: 1) feature pyramid network (FPN) fuses multi-stage features extracted from the backbone network; 2) scale-selection dilated layer (SSDL) extracts features by using different dilated convolution kernels for each stage; 3) perspective-embedded fusion layer (PEFL) encodes the spatial perspective relationships across all feature levels into the network and provides a more effective fine-grained weight map; and 4) density maps generator (DMG) employs deconvolution layer as a decoder to generate high-quality density maps. The above strategies maximizes the ability of multi-column network to extract the features of instances with various scales. Extensive experiments on seven crowd counting benchmark datasets, JHU-CROWD, ShanghaiTech, UCF-QNRF, ShanghaiTechRGBD, WorldEXPO’10, TRANCOS, and NWPU-Crowd indicate that PESSNet achieves reliable recognition performance and high robustness in difference crowd counting.