Hyperspectral images (HSIs) with high spatial resolution are challenging to obtain directly due to sensor limitations. Deep learning is able to provide an end-to-end reconstruction solution from low to high spatial resolution. Nevertheless, existing deep learning-based methods have two main drawbacks. First, deep networks with self-attention mechanisms often require a trade-off between internal resolution, model performance and complexity, leading to the loss of fine-grained, high-resolution features. Second, there are visual discrepancies between the reconstructed hyperspectral image (HSI) and the ground truth because they focus on spatial-spectral domain learning. In this paper, a novel super-resolution algorithm for HSIs, named SRDNet, is proposed by using a dual-domain network with hybrid convolution and progressive upsampling to exploit both spatial-spectral and frequency information of the hyperspectral data. In this approach, we design a self-attentive pyramid structure (HSL) to capture interspectral self-similarity in the spatial domain, thereby increasing the receptive range of attention and improving the feature representation of the network. Additionally, we introduce a hyperspectral frequency loss (HFL) with dynamic weighting to optimize the model in the frequency domain and improve the perceptual quality of the HSI. Experimental results on three benchmark datasets show that SRDNet effectively improves the texture information of the HSI and outperforms state-of-the-art methods. The code is available at https://github.com/LTTdouble/SRDNet.

The field of remote sensing change detection (RSCD) has seen significant advancements recently, focusing on the precise identification and analysis of temporal changes in remote sensing images. Existing deep learning-based RSCD methods primarily rely on concatenation or subtraction to integrate features of bi-temporal images and reconstruct change features through a feature pyramid network (FPN) decoding architecture. However, these methods face challenges related to inadequate spatio-temporal change representation and insufficient aggregation of multilevel semantic information, resulting in pseudo-changes and poor completeness of detected change objects. In this article, we propose an innovative RSCD framework via spatio-temporal feature fusion and guide aggregation (STFF-GA) to address the aforementioned challenges. The architecture of this network comprises two key components: the STFF module and the GA module. The STFF module is designed as a low-parameter and low-computation structure, effectively enhancing the representation of spatio-temporal change information through split, interaction, and fusion strategies. The GA module uses deep feature guidance (DFG) mapping as prior information to guide the aggregation of multilevel semantic information, thereby correcting the positional information of change objects and filtering out pseudo-changes and other noise interference. In addition, it utilizes convolution kernels of various scales to extract fine-grained features, facilitating the complete reconstruction of change objects. Extensive experiments conducted on three benchmark change detection datasets demonstrate that the proposed STFF-GA consistently outperforms other state-of-the-art (SOTA) detectors. The code is available athttps://github.com/NjustHGWei/STFF-GA.

Change detection (CD) in remote sensing (RS) images is a technique used to analyze and characterize surface changes from remotely sensed data at different time periods. However, current deep-learning-based methods sometimes struggle with the diversity of targets in complex RS scenarios, leading to issues, such as false detections and loss of detail. To address these challenges, we propose a method called difference learning-based convolution and network (DLB-CNet) for building CD (BCD). In DLB-CNet, we use difference learning module (DLM), accomplishing the extraction of building change features by enhancing the feature differences between the two images and enhancing model robustness. Additionally, an innovative attention module called integration attention (IA) is introduced to efficiently process semantic information by jointly focusing on global representation subspaces. Our model achieves impressive results on the LEVIR-CD dataset, WHU-CD dataset, and CDD dataset, with${F}1$-scores of 90.56%, 92.28%, and 94.98%, respectively, demonstrating its superiority over the state-of-the-art methods.

The spatial fixed-pattern noise (FPN) compromises severely the quality of the acquired imagery, even makes such images inappropriate for some applications. In order to lower the FPN, some critical nonuniformity correction (NUC) algorithms such as NUC based on linear model, scene-based NUC and so on have been developed. But each algorithm has some drawbacks: restricted application in small dynamic range of objects temperature, low performance under the drift of the environment temperature and complex calculations. In these cases, we develop a bivariate and quadratic model of the FPA and the NUC technique based on the model. The proposed method does not need any assumptions and is a good solution for hardware implementation. It overcomes the drawbacks of the critical algorithm mentioned above. The last simulations and experiments show that the proposed algorithm exhibits a superior correction effect in both large objects temperature range and environment temperature range.