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.

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.

Aircraft classifiers in remotely sensed images based on deep convolutional neural networks play a significant role in military. However, in practical applications, there is a lack of remote sensing fine-grained aircraft data. In this study, we demonstrate that few-shot learning (FSL) can be effectively used for fine-grained identification of aircraft and propose a new classifier-adaptive earth mover’s distance (Adap-EMD) for recognition of few-sample fine-grained aircraft. Adap-EMD consists of an efficient block attention mechanism (EBAM) and an adaptive feature measurement filter (AFMF). The EBAM effectively fuses channel and spatial correlation to capture global features with more pixel-wise relevance and contextual information. The non-parametric AFMF expresses the key information from the adapted emphasizing feature map to achieve a more accurate similarity measurement. Our model outperforms state-of-the-art models on a major few-shot aircraft fine-grained recognition benchmark dataset, introducing only a few additional computations.

The availability of many commercial satellites has created favorable conditions for tracking typical objects in remote sensing sequences, making them widely useful in numerous applications. However, small objects, multiple similar disruptors, background clutter, and occlusion are significant challenges to this field. This study proposes the novel tracker-temporal motion compensation Siamese network (Siam-TMC) for remote sensing tracking. Our method relies on a multidimensional information-aware module and a temporal motion compensation mechanism. Notably, we propose a dual branch-based Dim-Aware module that brings together foreground and high frequency information to distinguish between critical small objects and interferers. In addition, a TMComp mechanism using temporal motion information was designed to mitigate object trajectory drift through the supervision of occlusion detection. Detailed experimental comparisons on a benchmark dataset show that our method outperforms the state-of-the-art tracking models, particularly in occlusion scenarios.

Interpreting airborne remote sensing images plays an important role in aviation control and battlefield situational awareness. However, highly dynamic aircraft detection remains challenging, owing to variable object sizes, flexible attitudes, and motion blur. This study develops a multi-scale airborne aircraft dataset benchmark to overcome aircraft detection challenges, such as high intraclass variance, multiple scales and angles, motion blur, and partial occlusion. We also propose a trained-from-scratch aircraft detector, the local feature correlation single shot multibox detector (LFC-SSD), to detect multi-scale aircraft. The LFC-SSD comprises a local correlation feature extraction module, called “Right-Residual,” and a feature fusion module using a reverse feature pyramid network (R-FPN). Right-Residual extends the global receptive field by aggregating contextual information while learning non-adjacent region features efficiently. R-FPN utilizes multi-path information transfer horizontally with recursive integration to enhance the robust representation of the location information of multi-scale object features. In addition, a specific default boxes method is designed for remote-sensing images of aircraft. Extensive experimental results confirm the significant improvement of the proposed method over several existing state-of-the-art methods.

Remote sensing image ship fine-grained classification is a challenging vision problem due to factors such as inter-class similarity, real-world image scarcity, and class imbalance. Data augmentation aims to solve these problems from the data perspective. Most of these methods cannot work effectively in complex scenes, which restricts their practical application. Here, we propose a novel data augmentation pipeline based on local-aware image translation to achieve the representation mapping between cross-domain corresponding instances. Our pipeline contains three modules: Imaging Simulation System (ISS), Local-aware Progressive Image-to-Image Translation (LoPIT), and Image Harmonization (IH) modules. The ISS module generates simulated images with correct appearance and diverse features based on the input requirement information. To tackle the domain gap between simulated image and real-world image, we propose the Local-aware CycleGAN in the LoPIT module to achieve mapping based on local-aware learning and apply two sub-modules to progressively complete the remote sensing image global cross-domain translation. The IH module uses image harmonization technology to coordinate the visual appearance between the foreground and background to generate sufficient remote sensing ship images with precise representation, photorealistic style, and harmonious features. Moreover, we present a mixed dataset including real-world images and our synthetic images for remote sensing image fine-grained ship classification. Our dataset named RSSA-12 contains 12 categories of ship targets in 3831 images with high-quality annotated category labels, effectively alleviating the long-tail problem of existing datasets. Experimental results demonstrate that our progressive pipeline outperforms the state-of-the-art data augmentation method on the remote sensing fine-grained ship classification task.

Numerical simulation of thermal convection in the Earth's outer core requires extreme-scale computing due to the large temporal and spatial disparity, extreme physical parameters, rapid rotation and spherical geometry. In this work, the numerical simulation of the thermal convection in the Earth's outer core for CPU-MIC heterogeneous many-core systems is studied. Firstly, starting from a legacy parallel code based on the PETSc software package, a framework of the numerical simulation built on CPU-MIC heterogeneous many-core systems has been developed. Secondly, a sparse linear solver for CPUMIC heterogeneous many-core systems, which focuses on solving the two linear systems of the simulation, is presented and optimized. Thirdly, some computational kernels of the simulation, including sparse matrix-vector multiplication (SpMV) and polynomial preconditioner on distributed memory Xeon Phiaccelerated systems are implemented and optimized. In addition, in order to reduce the cost of data movement, we use methods to minimize the memory access, the PCI-E data transfer, and the MPI communication. Finally, some optimized measures are taken to the extended code. Experiments on Tianhe-2 Supercomputer show that as compared to the original code, our Xeon Phiaccelerated design is able to deliver 6.93x and 6.00x speedups for single MIC device and 64 MIC devices, respectively.

Large-scale simulation of the thermal convection in the Earth's outer core is studied. Starting from a legacy parallel code using Aztec and MPI, two optimized codes have been developed based on the PETSc software package. The first version gains several times acceleration with the help of the block-Jacobi preconditioners and the well-optimized libraries provided in PETSc. The second version, aiming at better parallel scalability, is developed based on the ideas of domain decomposition method for multi-physical problems. Test results employing thousands of processor cores on three supercomputers, i.e., an IBM Blue Gene/L, a Dawning 5000A and a Lenovo DeepComp 7000, are provided.

In this paper, a new nonlinear magnification method is proposed. The new method produces the effect of nonlinear magnification based on perspective projection and the Bezier curve is used as drop-off function. So the new method can enhance the local information and keep the global context. It can provide different representation by adjusting the distortion degree of nonlinear magnification especially. In this way, image interpretation and target recognition would be performed effectively.

In this paper a new method is proposed to solve the problem of high resolution satellite imagery without GCPs by considering the consecutive imaging parameters. That is, the imaging parameters of consecutive imagery are calculated based on GCPs in order to set up the prediction formula, and then the imaging parameters of high resolution imagery can be forecasted. Thus the rigorous model is introduced to precisely locate imagery. QuickBird imagery is test and geo-referencing accuracy reaches 3-4 pixels.