Recently, the Transformer-based technique has emerged as a promising solution for modeling contextual information in Remote Sensing (RS) scenes and has found widespread applications in RS scene classification. However, how to make full use of intermediate features learned in Transformers is of crucial importance in the RS scene classification tasks. Therefore, this paper proposes a Hierarchical Feature Fusion of Transformer with Patch Dilating (HFFT-PD), which aims to capture rich contextual information from hierarchical features to enhance the performance of RS scene classification. Specifically, the HFFT-PD model consists of a Hierarchical Transformer Merging (HTM) block and a Lightweight Adaptive Channel Compression (LACC) module, in which the HTM is specially designed for the Transformer architecture to bridge the semantic gaps between features from different hierarchical blocks, and the LACC accounts for the significance of distinct channels in the ultimate classification features. In addition, a brand-new Patch Dilating strategy is uniquely designed for the Transformer paradigm, functioning as a reassembly operator predicated on patch features. Contrasting with conventional upsampling techniques, Patch Dilating facilitates upsampling without requiring supplementary information, while concurrently preserving the semantic content of local spatial structure. Extensive and rigorous experiments conducted on the UCM, AID, and NWPU-45 datasets, with training ratios of 80%, 50%, and 20% respectively, demonstrate that our proposed HFFT-PD outperforms the baseline at least by 0.59%, 0.44%, and 0.99% respectively, showcasing the significant superiority of our HFFT-PD over contemporary state-of-the-art methodologies.

Aerial visible-to-infrared image translation technology aims to generate infrared images from visible inputs, effectively expanding the acquisition of infrared images in complex aerial remote sensing scenarios. Although existing CNN-based approaches demonstrate proficiency in capturing local spatial details, they often fail to model global scene context, which is crucial for maintaining structural consistency in complex aerial scenes. To address this challenge, a hybrid architecture network is proposed to model long-range dependencies while preserving fine-grained local details via an encoder-decoder framework. We further introduce a Dynamic Parallel Window-based Attention mechanism, which dynamically parallelizes window-based and shifted window-based multi-head self-attention in separate streams across transformer blocks to enhance global context modeling. Additionally, a masked image pre-training framework with wavelet transform loss is designed to guide multi-scale feature alignment and high-frequency detail reconstruction, effectively addressing the texture discrepancy between visible and infrared modalities. Extensive experiments conducted on the upgraded AVIID and DroneVehicle benchmark datasets demonstrate that our method significantly outperforms current state-of-the-art approaches in terms of both visual quality and perceptual metrics. The code of USTNet and the upgraded AVIID dataset are available at https://github.com/silver-hzh/USTNet.

Due to the wide application of Remote Sensing (RS) image scene classification, more and more scholars activate great attention to it. With the development of the Convolutional Neural Network (CNN), the CNN-based methods of RS image scene classification have made impressive progress. In the existing works, most of the architectures just considered the global information of the RS images. However, the global information contains a large number of redundant areas that diminish the classification performance and ignore the local information that reflects more fine spatial details of local objects. Furthermore, most CNN-based methods assign the same weights to each feature vector causing the mode to fail to discriminate the crucial features. In this paper, a novel method by Two-branch Deep Feature Embedding with a Dual Attention-Aware module for RS image scene classification is proposed. In order to mine more complementary information, we extract global semantic-based features of high level and local object-based features of low level by the Two-branch Deep Feature Embedding (TDFE) module. Then, to focus selectively on the key global-semantics feature maps as well as the key local regions, we propose a Dual Attention-Aware (DAA) module to attain those key information. We conduct extensive experiments to verify the superiority of our proposed method, and the experimental results obtained on two widely used RS scene classification benchmarks demonstrate the effectiveness of the proposed method.

In the global landscape of the Internet of Things (IoT), drone IoT technology has gained widespread application. This technology can monitor and analyze land use and land cover more quickly and more accurately. Currently, the images collected by drone IoT technology are mostly visible images, which are highly susceptible to external environmental factors, while the acquisition of infrared images is relatively more challenging. Visible-to-infrared drone image translation seeks to convert visible drone images into their corresponding infrared counterparts. Although existing generative adversarial network-based image-to-image translation methods have demonstrated impressive results in the domain of natural images, they still face challenges in generating highly realistic infrared drone images. Therefore, a novel multiscale feature decomposition (MSFD) method is introduced for visible-to-infrared drone image translation. The proposed approach accomplishes the translation through spectral feature disentanglement and cross-modal recombination. In our model, spectral feature disentanglement is based on the separation of modality-specific spectral information and modality-invariant shared structural content from the image representation. Subsequently, the spectral features and underlying content from different modalities can be recombined by generators to facilitate cross-modality image translation. To enhance the quality of generated images, our method integrates a multiscale spectral feature encoder to address significant spectral discrepancies between targets and backgrounds in drone images by extracting and fusing spectral features at different scales. Additionally, the strategy of multiscale generators and discriminators further enhances the generation quality of infrared drone images. The experimental results highlight the superior performance of our model in visible-to-infrared drone image translation.

As a branch of multicultural literature, bilingual children’s picture books present a special opportunity for readers to expand their horizons and knowledge of other cultures. The researchers took a closer look at the text quality of 31 English/Chinese bilingual children’s picture books. These bilingual books were examined on the aspects of the English text’s quality, the Chinese text’s quality, and the correspondence between the English text and the Chinese text. The findings suggested that while the English text had no obvious issues, the Chinese text lacked quality. In addition, the researchers noticed that the Chinese text appeared subordinate when comparing the two texts.