Cloud detection is one of the key technologies in the field of remote sensing. Although extensive deep learning-based cloud detection methods achieve good performance, their detection results in confusing areas such as cloud boundaries and thin clouds are often not satisfactory due to the potential inter-class similarity and intra-class inconsistency of objects. To this end, we propose a Boundary-Aware Bilateral Fusion network (BABFNet), which effectively enhances cloud detection in confusing areas by introducing a boundary prediction branch as an auxiliary. To avoid the loss of details, the boundary prediction branch is designed to run at full resolution with a shallow architecture, while some Semantic Enhancement Modules (SEMs) are used to supplement high-level semantic information by introducing multi-level encoder features of the cloud detection branch. This feature sharing in turn drives the cloud detection branch to focus more on cloud boundaries during training. At the end of the network, a Bilateral Fusion Module (BFM) is added for information complementarity between features from these two branches. The features from the cloud detection branch provide multi-scale features to the boundary prediction branch for more accurate boundary prediction, while the features from the boundary prediction branch further serve as prior knowledge to help the cloud detection branch aggregate contextual information. To verify the effectiveness of the proposed method, we select four different networks as cloud detection branches and conduct comparative experiments on two public datasets, GF-1 WFV and MODIS. The experimental results show that the proposed method significantly enhances cloud detection in confusing areas.

With the continuous improvement of high-resolution remote-sensing image-acquisition technologies, image quality and resolution are constantly improved, which greatly promotes the development of object detection in large-scale remote sensing images. However, due to the memory constraints of Graphics Processing Units (GPUs), directly inputting large-scale remotesensing images into detectors poses significant challenges. Downsampling these images to sizes suitable for detectors often leads to the loss of critical details. Current mainstream object detection methods for large-scale images typically divide large images into patches and detect the objects of interest in all patches. These methods allocate computational resources uniformly on each patch, regardless of the presence of objects or variations in their density and size. This not only disrupts the delicate balance between accuracy and speed but also results in inefficient utilization of computational resources. To address these challenges, we propose a Situation-Aware Network (SANet) for object detection in large-scale remote sensing images. SANet introduces a Situation-Aware (SA) module designed to filter out invalid information in large-scale images, thereby reducing computational complexity. Moreover, it integrates a RegionAdaptive (RA) module, which dynamically adjusts the level of granularity for object regions and allocates them to appropriate detectors, thus minimizing resource wastage. Our method has been tested on challenging datasets such as DOTA-v1.0, DOTAv1.5, DOTA-v2.0, and TT100K, demonstrating consistent advantages in both speed and accuracy compared to existing object detection methodologies.

JPEG-LS (a lossless (LS) compression standard developed by the Joint Photographic Expert Group) compressed image restoration is a significant problem in remote sensing applications. It faces the following two challenges: first, bridging small pixel-value gaps from wide numerical ranges; and second, removing banding artifacts in the condition of lacking available context information. As far as we know, there is currently no research dealing with the above issues. Hence, we develop this initial line of work on JPEG-LS compressed remote sensing image restoration. We propose a novel CNN model called CARNet. Its core idea is a context-aware residual learning mechanism. Specifically, it realizes residual learning for accurate restoration by adopting a scale-invariant baseline. It enables large receptive fields for banding artifact removal through a context-aware scheme. Additionally, it eases the information flow among stages by utilizing a prior-guided feature-fusion mechanism. Alternatively, we design novel R IQA models to provide a better restoration performance assessment for our study by utilizing gradient priors of JPEG-LS banding artifacts. Furthermore, we prepare a new dataset of JPEG-LS compressed remote sensing images to supplement existing benchmark data. Experiments show that our method sets the state-of-the-art for JPEG-LS compressed remote sensing image restoration.

With the development of intelligent education, it has become one of the more efficient learning schemes to construct the knowledge graph which can excavate the knowledge base. People generally use RDF triples and use languages such as OWL to construct knowledge graphs, but this method has problems such as limited expression ability and too much manual annotation. In this paper, we propose a framework that combines statistical language models, neural network language models, and clustering and clipping algorithms. When processing unstructured text data, unsupervised extraction of representative key words as features of the structured graphs’ entities, so that the processed entity information has more accurate semantics and human learning relevance, and the method of clustering and calculating the learning rate is used to further clarify the learning order and mastery of knowledge points. We have conducted extensive experiments to collect data from the two major modules of HPC high-performance computing courses and domains as datasets, and used this framework to build many knowledge graphs which can provide practical learning. The knowledge graph of this paper can be obtained from: https://v2.easyhpc.net:10000/knowledge Knowledge Graph Module.

SignificanceFluorescence molecular tomography (FMT) is a promising imaging modality, which has played a key role in disease progression and treatment response. However, the quality of FMT reconstruction is limited by the strong scattering and inadequate surface measurements, which makes it a highly ill-posed problem. Improving the quality of FMT reconstruction is crucial to meet the actual clinical application requirements.AimWe propose an algorithm, neighbor-based adaptive sparsity orthogonal least square (NASOLS), to improve the quality of FMT reconstruction.ApproachThe proposed NASOLS does not require sparsity prior information and is designed to efficiently establish a support set using a neighbor expansion strategy based on the orthogonal least squares algorithm. The performance of the algorithm was tested through numerical simulations, physical phantom experiments, and small animal experiments.ResultsThe results of the experiments demonstrated that the NASOLS significantly improves the reconstruction of images according to indicators, especially for double-target reconstruction.ConclusionNASOLS can recover the fluorescence target with a good location error according to simulation experiments, phantom experiments and small mice experiments. This method is suitable for sparsity target reconstruction, and it would be applied to early detection of tumors.