Hyperspectral images (HSIs) are widely used as data formats for remote sensing. Correspondingly, the spectral super-resolution (SSR) technique used to generate high-spatial-resolution HSIs from high-spatial-resolution remote sensing multispectral images (MSIs) has emerged as a popular research topic owing to its high costs and hardware requirements. Generally, existing SSR methods obtain an HSI from the MSI of natural scenes. However, these methods barely learn the complex spectra found in remote sensing images and lack effective treatments for the degradation phenomenon in remote sensing scenes. In this study, a spectral-learning-based transformer network composed of a spectral-response-function (SRF)-guided multilevel feature extraction module (MFEM) and spectral nonlinear mapping learning module (NMLM) is cascaded. The NMLM uses different blocks to gradually learn spatial and spectral information from remote sensing images. Additionally, we focus on atmospheric effects and other factors that influence the generation of remote sensing images and design an MFEM to eliminate these effects. To augment the spectral dimension, an SRF is precisely added to the MFEM as a guide. Experimental results on the Obita Hyperspectral Satellites, Pavia Center, and Washington DC Mall datasets reveal that our method outperforms other state-of-the-art methods in terms of the root mean square error, mean relative absolute error, and relative root mean square error.

Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the accuracy and efficiency of ship target detection algorithms. Most existing target detection methods perform well in conditions of a general scenario but underperform in complex conditions. In this study, a collaborative network for target detection under foggy weather conditions is proposed, aiming to achieve improved accuracy while satisfying the need for real-time detection. First, a collaborative block was designed and SCConv and PCA modules were introduced to enhance the detection of low-quality images. Second, the PAN + FPN structure was adopted to take full advantage of its lightweight and efficient features. Finally, four detection heads were used to enhance the performance. In addition to this, a dataset for foggy ship detection was constructed based on ShipRSImageNet, and the mAP on the dataset reached 48.7%. The detection speed reached 33.3 frames per second (FPS), which is ultimately comparable to YOLOF. It shows that the model proposed has good detection effectiveness for remote sensing ship images during low-contrast foggy days.

Temperature field calculation is an important step in infrared image simulation. However, the existing solutions, such as heat conduction modelling and pre-generated lookup tables based on temperature calculation tools, are difficult to meet the requirements of high-performance simulation of infrared images based on three-dimensional scenes under multi-environmental conditions in terms of accuracy, timeliness, and flexibility. In recent years, machine learning-based temperature field prediction methods have been proposed, but these methods only consider the influence of meteorological parameters on the temperature value, while not considering the geometric structure and the thermophysical parameters of the object, which results in the low accuracy. In this paper, a multivariate temperature field prediction network based on heterogeneous data (MTPHNet) is proposed. The network fuses geometry structure, meteorological, and thermophysical parameters to predict temperature. First, a Point Cloud Feature Extraction Module and Environmental Data Mapping Module are used to extract geometric information, thermophysical, and meteorological features. The extracted features are fused by the Data Fusion Module for temperature field prediction. Experiment results show that MTPHNet significantly improves the prediction accuracy of the temperature field. Compared with the v-Support Vector Regression and the combined back-propagation neural network, the mean absolute error and root mean square error of MTPHNet are reduced by at least 23.4% and 27.7%, respectively, while the R-square is increased by at least 5.85%. MTPHNet also achieves good results in multi-target and complex target temperature field prediction tasks. These results validate the effectiveness of the proposed method.

Camouflaged object detection (COD) aims to accurately detect objects that blend in with their backgrounds. While deep learning-based methods have significantly improved detection accuracy, the persistent similarity in chromatic and textural characteristics between camouflaged objects and their environments continues to pose substantial challenges, particularly in complex scenarios. Existing COD methods still encounter two primary challenges: (1) the difficulty in effectively fusing detailed features with semantic representations, leading to suboptimal balance between local and global features in the fused features; (2) progressive information dilution in conventional top-down decoder architectures, resulting in imprecise boundary localization. To address these dual challenges of multi-scale feature enhancement and semantic information dilution, we propose a D ual-Branch C ross-Layer I nformation F low Network (DCIFNet) for camouflaged object detection in complex scenes. Specifically, our framework integrates a hybrid CNN-ViT architecture for parallel extraction of localized patterns and global contextual features. Subsequently, a global local fusion (GLF) module is employed to iteratively and adaptively fuse the global and local features at each stage using gating weights. Additionally, a novel Dense Connection Decoder (DCD) module is designed to mitigate the information dilution problem associated with traditional top-down decoders. Extensive experiments show that DCIFNet outperforms other mainstream COD models in most performance metrics on four widely used benchmark datasets. On the COD10K dataset, compared with the latest SOTA work of PRNet, DCIFNet improves the metrics of \({S_{\alpha}}\) , \({E_{\phi}}\) , and \(F_{\beta}^{w}\) by 0.11 \(\%\) , 0.11 \(\%\) , and 1.38 \(\%\) , respectively, demonstrating a significant performance enhancement. The code is available https://github.com/WateverOk/DCIFNet .

Remote sensing images (RSIs) are limited by low-resolution imaging and a variety of degradation effects, posing significant challenges for high-level visual tasks. Traditional deep learning-based super-resolution (SR) algorithms struggle to optimize these issues end-to-end, which inhibits the algorithm’s lightweight design and restricts its application in real-time scenarios. To address this, we introduce a multi-branch attention integration network (MAIN) for the degraded RSI SR. This network features two key functional components: the multi-scale feature perception (MsFP) module and the multi-branch attention integration block (MAIB). The MsFP module, a lightweight convolution-based architecture, is designed primarily to counteract degradation effects and depict low-dimensional features. MAIB, a multi-branch parallel layout, employs attention mechanisms to enable high-level semantic information extraction and context-aware learning. Furthermore, we utilize modulation transfer functions to emulate various degradation effects, thus creating a dedicated dataset for degraded RSI SR, called DeRSSA. Extensive experimental evidence shows that MAIN exceeds the performance of the existing state-of-the-art method for the degraded RSI SR task. Our code and dataset will be accessible at https://github.com/lbo0928/MAIN.