Super-Resolution for remote sensing has the potential for huge impact on diverse applications by reconstructing an accurate high-resolution image from its low-resolution counterpart. Despite a lot of attention, most recent methods concentrate on the overall modeling of the original inputs, treat various frequency information within the image equally, which can lead to distortion of image details or artefacts. To tackle this issue, we proposed a frequency-domain decoupling approach for image super-resolution. It uses nonparametric clustering to obtain the low-frequency contours, applies a linearized Transformer to model the long-range dependency of the mid-frequency details, processes the high-frequency textural with the attention mechanism, and employs Fourier transform to integrate different frequency information effectively. By processing the different frequencies independently, we achieve a more accurate enhancement of the original scene visual quality without adding noise. We conducted evaluations of our method across five real-world scenarios with distinct characteristics. The results show that our model has a significant improvement in peak signal-to-noise ratio of 2.4-5.5 dB over the state-of-the-art method.

Remote sensing imagery (RSI) segmentation plays a crucial role in environmental monitoring and geospatial analysis. However, in real-world practical applications, the domain shift problem between the source domain and target domain often leads to severe degradation of model performance. Most existing unsupervised domain adaptation methods focus on aligning global-local domain features or category features, neglecting the variations of ground object categories within local scenes. To capture these variations, we propose the scene covariance alignment (SCA) approach to guide the learning of scene-level features in the domain. Specifically, we propose a scene covariance alignment model to address the domain adaptation challenge in RSI segmentation. Unlike traditional global feature alignment methods, SCA incorporates a scene feature pooling (SFP) module and a covariance regularization (CR) mechanism to extract and align scene-level features effectively and focuses on aligning local regions with different scene characteristics between source and target domains. Experiments on both the LoveDA and Yanqing land cover datasets demonstrate that SCA exhibits excellent performance in cross-domain RSI segmentation tasks, particularly outperforming state-of-the-art baselines across various scenarios, including different noise levels, spatial resolutions, and environmental conditions.

Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field.