Continuous Remote Sensing Image Super-Resolution Based on Context Interaction in Implicit Function Space

Chen, Keyan; Li, Wenyuan; Lei, Sen; Chen, Jianqi; Jiang, Xiaolong; Zou, Zhengxia; Shi, Zhenwei

doi:10.1109/tgrs.2023.3272473

Cited by 23 publications

(3 citation statements)

References 62 publications

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“…In addition to these algorithms that have been applied to astronomical images, image enhancement or super-resolution algorithms in other fields also have great reference value [30][31][32][33]. They use the attention mechanism of transformers whose characteristics are highly beneficial for background modeling and global modeling.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Level Convolutional Network for Ground-Based Star Image Enhancement

Liu

Niu

et al. 2023

Remote Sensing

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The monitoring of space debris is important for spacecraft such as satellites operating in orbit, but the background in star images taken by ground-based telescopes is relatively complex, including stray light caused by diffuse reflections from celestial bodies such as the Earth or Moon, interference from clouds in the atmosphere, etc. This has a serious impact on the monitoring of dim and small space debris targets. In order to solve the interference problem posed by a complex background, and improve the signal-to-noise ratio between the target and the background, in this paper, we propose a novel star image enhancement algorithm, MBS-Net, based on background suppression. Specifically, the network contains three parts, namely the background information estimation stage, multi-level U-Net cascade module, and recursive feature fusion stage. In addition, we propose a new multi-scale convolutional block, which can laterally fuse multi-scale perceptual field information, which has fewer parameters and fitting capability compared to ordinary convolution. For training, we combine simulation and real data, and use parameters obtained on the simulation data as pre-training parameters by way of parameter migration. Experiments show that the algorithm proposed in this paper achieves competitive performance in all evaluation metrics on multiple real ground-based datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Level Convolutional Network for Ground-Based Star Image Enhancement

Liu

Niu

et al. 2023

Remote Sensing

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“…Pixel-based methods reduce the degree of human intervention through supervised training and improve computational efficiency [22], but it is difficult to obtain clear landslide boundaries and they cannot fully utilize the rich structural and textural information in the images [23]. The object-based method utilizes a variety of discriminative features, such as spectral features, texture features, and morphological features of landslides, for landslide detection [24][25][26]. This method is capable of categorizing objects with similar features into the same class, reducing salt-and-pepper noise.…”

Section: Introductionmentioning

confidence: 99%

SAM-CFFNet: SAM-Based Cross-Feature Fusion Network for Intelligent Identification of Landslides

Xi,

Yu,

et al. 2024

Remote Sensing

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Landslides are common hazardous geological events, and accurate and efficient landslide identification methods are important for hazard assessment and post-disaster response to geological disasters. Deep learning (DL) methods based on remote sensing data are currently widely used in landslide identification tasks. The recently proposed segment anything model (SAM) has shown strong generalization capabilities in zero-shot semantic segmentation. Nevertheless, SAM heavily relies on user-provided prompts, and performs poorly in identifying landslides on remote sensing images. In this study, we propose a SAM-based cross-feature fusion network (SAM-CFFNet) for the landslide identification task. The model utilizes SAM’s image encoder to extract multi-level features and our proposed cross-feature fusion decoder (CFFD) to generate high-precision segmentation results. The CFFD enhances landslide information through fine-tuning and cross-fusing multi-level features while leveraging a shallow feature extractor (SFE) to supplement texture details and improve recognition performance. SAM-CFFNet achieves high-precision landslide identification without the need for prompts while retaining SAM’s robust feature extraction capabilities. Experimental results on three open-source landslide datasets show that SAM-CFFNet outperformed other comparative models in terms of landslide identification accuracy and achieved an intersection over union (IoU) of 77.13%, 55.26%, and 73.87% on the three datasets, respectively. Our ablation studies confirm the effectiveness of each module designed in our model. Moreover, we validated the justification for our CFFD design through comparative analysis with diverse decoders. SAM-CFFNet achieves precise landslide identification using remote sensing images, demonstrating the potential application of the SAM-based model in geohazard analysis.

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“…In remote sensing, Pan-Mamba [8] proposed channel swapping Mamba and cross-modal Mamba for pan-sharpening. RSMamba [9] presented a multi-path VSS Block for large-scale image interpretation. These two methods directly replace the existing network with the VSS blocks.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Domain Adaptation Augmented by Mutually Boosted Attention for Semantic Segmentation of VHR Remote Sensing Images

Zhang

Wang

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Semantic segmentation of remote sensing images is a fundamental task in geoscience research. However, there are some significant shortcomings for the widely used convolutional neural networks (CNNs) and Transformers. The former is limited by its insufficient long-range modeling capabilities, while the latter is hampered by its computational complexity. Recently, a novel visual state space (VSS) model represented by Mamba has emerged, capable of modeling long-range relationships with linear computability. In this work, we propose a novel dual-branch network named remote sensing images semantic segmentation Mamba (RS3Mamba) to incorporate this innovative technology into remote sensing tasks. Specifically, RS3Mamba utilizes VSS blocks to construct an auxiliary branch, providing additional global information to convolution-based main branch. Moreover, considering the distinct characteristics of the two branches, we introduce a collaborative completion module (CCM) to enhance and fuse features from the dual-encoder. Experimental results on two widely used datasets, ISPRS Vaihingen and LoveDA Urban, demonstrate the effectiveness and potential of the proposed RS3Mamba. To the best of our knowledge, this is the first vision Mamba specifically designed for remote sensing images semantic segmentation. The source code will be made available at https://github.com/sstary/SSRS.

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Continuous Remote Sensing Image Super-Resolution Based on Context Interaction in Implicit Function Space

Cited by 23 publications

References 62 publications

Multi-Level Convolutional Network for Ground-Based Star Image Enhancement

Multi-Level Convolutional Network for Ground-Based Star Image Enhancement

SAM-CFFNet: SAM-Based Cross-Feature Fusion Network for Intelligent Identification of Landslides

Unsupervised Domain Adaptation Augmented by Mutually Boosted Attention for Semantic Segmentation of VHR Remote Sensing Images

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