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A Novel Aerosol Retrieval Method Based on Joint Polarization and Intensity Data of Synchronization Monitoring Atmospheric Corrector (SMAC) Onboard high-Spatial-Resolution GFDM Satellite

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Ge, Bangyu Wang, Xia Li, Zhengqiang Zhou, Ping Li, Guoyuan Hou, Weizhen Qiu, Zhenwei Xiao, Chenchao Liu, Shuhan Yue, Qingxing Wang, Haofei Xie, Yisong Minist Nat Resources Peoples Republ China Land Satellite Remote Sensing Applicat Ctr Beijing 100048 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Aerosp Informat Res Inst Beijing 100101 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Key Lab Opt Calibrat & Characterizat Hefei 230031 Peoples R China China Meteorol Adm Natl Satellite Meteorol Ctr Nat Ctr Space Weather Innovat Ctr Fengyun Meteorol Satellite FYSICKey Beijing 100101 Peoples R China

Synchronization monitoring atmospheric corrector (SMAC) sensor is equipped with polarization and intensity data, providing the possibility of retrieving more accurate aerosol parameters for main sensor's atmospheric correction. In this study, a novel aerosol retrieval algorithm based on intensity and polarization data was proposed. First, we analyze and construct the ratio relationship of different intensity and polarization channels of SMAC on different surface types and analyze the correlation between these ratios and normalized difference vegetation index (NDVI) and scattering angle (SCA). Then, for the first time, high-precision surface prior knowledge for SMAC aerosol retrieval is constructed, and then, intensity and polarization data were used to retrieve high-precision aerosol products simultaneously. These results are compared and validated with Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based observations aerosol products, and aerosol optical depth (AOD) product comparison between the SMAC and MODIS showed that they had similar spatial distribution, scattered dots of them with a Pearson correlation coefficient (R) of 0.84 and a root-mean-square error (RMSE) of 0.11. Meanwhile, their differences were also analyzed. The validation between the ground-based sites and the SAMC retrieval results also showed a good performance with R of 0.88 and RMSE of 0.10. These results revealed that the new algorithm is an effective method for retrieving reliable AOD products for the main sensor's atmosphere correction.

关键词： Aerosols Satellite broadcasting MODIS Reflectivity Accuracy remote sensing Land surface Cloud computing Spatial resolution Monitoring Aerosol retrieval intensity polarization surface prior knowledge synchronization monitoring atmospheric corrector (SMAC)

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An edge-aware high-resolution framework for camouflaged object detection

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IMAGE AND VISION computing 2025年 157卷

作者： Ma, Jingyuan Chen, Tianyou Xiao, Jin Hu, Xiaoguang Wang, Yingxun Beihang Univ Beijing 100191 Peoples R China Chinese Aeronaut Radio Elect Res Inst Shanghai 200241 Peoples R China Inst Remote Sensing Satellite Beijing 100094 Peoples R China

Camouflaged objects are often seamlessly assimilated into their surroundings and exhibit indistinct boundaries. The complex environmental conditions and the high intrinsic similarity between camouflaged targets and their backgrounds present significant challenges in accurately locating and fully segmenting these objects. Although existing methods have achieved remarkable performance across various real-world scenarios, they still struggle with challenging cases such as small targets, thin structures, and blurred boundaries. To address these issues, we propose a novel edge-aware high-resolution network. Specifically, we design a high-Resolution Feature Enhancement Module to exploit multi-scale features while preserving local details. Furthermore, we introduce an Edge Prediction Module to generate high-quality edge prediction maps. Subsequently, we develop an Attention-Guided Fusion Module to effectively leverage the edge prediction maps. With these key modules, the proposed model achieves real-time performance at 58 FPS and surpasses 21 state-of-the-art algorithms across six standard evaluation metrics. Source code will be publicly available at https://***/clelouch/EHNet.

关键词： Attention-guided fusion Camouflaged object detection Edge-aware segmentation Convolutional neural network

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VGRSS: Datasets and Models for Visual Grounding in remote sensing Ship Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Chen, Yaxiong Zhan, Liwen Zhao, Yichen Xiong, Shengwu Lu, Xiaoqiang Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China Wuhan Coll Interdisciplinary Artificial Intelligence Res Inst Wuhan 430212 Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350108 Peoples R China

This article introduces a task named visual grounding of remote sensing ship (VGRSS) images. The goal of VGRSS is to locate ship objects in remote sensing images guided by natural language. Extensive research has been conducted on multimodal processing of remote sensing images and text to retrieve rich information from remote sensing images using natural language. However, due to the unique characteristics of remote sensing ship images, ship localization using natural language remains a challenge. Therefore, in this work, we construct datasets for the VGRSS task and explore deep learning models. Specifically, our contributions can be summarized as follows: first, we construct two remote sensing ship datasets for visual grounding. One is based on the optical remote sensing ship target detection benchmark dataset, named RSSVG, while the other is based on the synthetic aperture radar (SAR) dataset, named SARVG. Second, we propose a language-guided visual feature enhancement (LVFE) module. This module enhances visual features through language guidance before visual-linguistic fusion (VLF). Third, we propose a VLF module based on multimodal feature stacking. This module inputs the stacked language and visual features, and then performs feature fusion using a Transformer, enabling effective cross-modal interaction and integration. Fourth, we introduce a novel loss calculation method by incorporating enhanced intersection over union (EIoU) into the loss function. Finally, we benchmark extensive state-of-the-art (SOTA) natural image visual grounding (VG) methods on the constructed RSSVG and SARVG datasets, then provide insightful analysis based on the results. This work offers valuable insights for developing better VGRSS models.

关键词： Visualization remote sensing Marine vehicles Grounding Transformers Benchmark testing Artificial intelligence Linguistics Feature extraction Accuracy Language-guided visual feature enhancement (LVFE) Transformer visual grounding (VG) dataset VG of remote sensing ship (VGRSS) images

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Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 273-293页

作者： Justo, Jon Alvarez Ghita, Alexandru Kovac, Daniel Garrett, Joseph L. Georgescu, Mariana-Iuliana Gonzalez-Llorente, Jesus Ionescu, Radu Tudor Johansen, Tor Arne Norwegian Univ Sci & Technol Dept Engn Cybernet N-7491 Trondheim Norway Univ Bucharest Dept Comp Sci Bucharest 030018 Romania Brno Univ Technol Dept Telecommun Brnostred 60190 Czech Republic Nihon Univ Dept Aerosp Engn Chiba 2758575 Japan

Satellites are increasingly adopting onboard AI to optimize operations and increase autonomy through in-orbit inference. The use of deep learning (DL) models for segmentation in hyperspectral imagery offers advantages for remote sensing applications. In this work, we train and test 20 models for multiclass segmentation in hyperspectral imagery, selected for their potential in future space deployment. These models include 1-D and 2-D convolutional neural networks (CNNs) and the latest vision transformers (ViTs). We propose a lightweight 1D-CNN model, 1D-Justo-LiuNet, which outperforms state-of-the-art models in the hypespectral domain. 1D-Justo-LiuNet exceeds the performance of 2D-CNN UNets and outperforms Apple's lightweight vision transformers designed for mobile inference. 1D-Justo-LiuNet achieves the highest accuracy (0.93) with the smallest model size (4563 parameters) among all tested models, while maintaining fast inference. Unlike 2D-CNNs and ViTs, which encode both spectral and spatial information, 1D-Justo-LiuNet focuses solely on the rich spectral features in hyperspectral data, benefitting from the high-dimensional feature space. Our findings are validated across various satellite datasets, with the HYPSO-1 mission serving as the primary case study for sea, land, and cloud segmentation. We further confirm our conclusions through generalization tests on other hyperspectral missions, such as NASA's EO-1. Based on its superior performance and compact size, we conclude that 1D-Justo-LiuNet is highly suitable for in-orbit deployment, providing an effective solution for optimizing and automating satellite operations at edge.

关键词： Clouds Image segmentation Satellites Hyperspectral imaging Data models Satellite broadcasting Artificial intelligence Earth Computational modeling Cloud computing 1D-CNNs 2D-CNNs deep learning (DL) remote sensing satellite hyperspectral imagery segmentation vision transformers (ViTs)

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The Application of Reverse Time Migration Surface Offset Gathers in Velocity Updating

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Zheng, Yingdong Liu, Guofeng Li, Bo China Univ Geosci Beijing Sch Geophys & Informat Technol Beijing 100083 Peoples R China SINOPEC Geophys Res Inst Nanjing 211103 Peoples R China

Reverse time migration (RTM) is a wave equation migration method that has high imaging accuracy and superior imaging effects in complex underground structures and has been extensively employed in industrial production. Surface offset gathers (SOGs) are crucial for migration velocity updating and muting the stretched waveform in the shallow layers of seismic data. However, RTM SOGs cannot be obtained directly. Usually, SOGs obtained through Kirchhoff migration are used for velocity updating, which fails to form a closed-loop process for RTM migration calculation and velocity updating. Attribute migration is a stable, simple, and efficient method to obtain RTM SOGs. Based on attribute migration, this article applied the constrained least-squares inversion instead of division to get a more accurate RTM SOGs and uses residual curvature analysis (RCA) to update the velocity. RTM SOGs can also be obtained by grouping seismic data according to offset and migrating each group separately. By comparison, SOGs obtained by these two methods had identical characteristics. Furthermore, the residual curvature spectrum after velocity analysis had identical characteristics. Therefore, we believe that the attribute migration method using constrained least-squares inversion can be employed for velocity updating and verify its effectiveness with model data and actual data. In summary, this article presents a stable and direct method to efficiently calculate SOGs for RTM and thus form a closed loop of RTM migration calculation and velocity updating.

关键词： Imaging Accuracy Geology Data models Geoscience and remote sensing Surface waves Stability analysis Sensitivity Production Surface treatment Attribute migration method residual curvature analysis (RCA) reverse time migration (RTM) surface offset gathers (SOGs) velocity updating

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Data processing system design methodology for high-orbit remote sensing satellites 3

Data processing system design methodology for high-orbit rem...

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2024 3rd International conference on Aerospace and Control Engineering, ICoACE 2024

作者： Li, Yongfeng Hao, Xiuqiang Zhang, Lang Xi’an710100 China

Aiming at the characteristics of high orbit remote sensing satellite, this paper proposes a data processing system design method that adopts a number of key technologies such as the highly reliable data interface, high fidelity image compression, multi-file storage management, adaptive rate control, etc., and realizes the error-free code exchange of high-speed remote sensing data among multiple devices with lower hardware resource overhead, has better image compression performance, and supports several tasks to be carried out in parallel without increasing additional hardware resources, and enables the effective frame efficiency of the transmission channel to reach 100%. The method proposed in this paper efficiently realizes the data processing and transmission requirements of high-orbit remote sensing satellites and significantly improves the application efficiency of high-orbit remote sensing satellites. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Image compression

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Integrated input system for partially coherent light control in optical neural networks for remote sensing

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OPTICS AND LASER TECHNOLOGY 2025年 189卷

作者： Li, Hongmin Liu, Xun Xu, Haonan Peng, Zhenxing Zhou, Xu Li, Wei Lu, Yao Wu, Qiang Xu, Jingjun Nankai Univ TEDA Inst Appl Phys Minist Educ Key Lab Weak Light Nonlinear Photon Tianjin 300457 Peoples R China Nankai Univ Sch Phys Tianjin 300457 Peoples R China China Acad Space Technol Beijing Inst Space Mech & Elect Beijing 100094 Peoples R China

Laser-based all-optical neural networks (ONNs) offer remarkable advantages in processing speed, interference resistance, and energy efficiency, making them highly applicable for fields such as machine vision and image classification recognition. However, in practical scenarios, laser coherence can degrade due to transmission interference, posing a significant challenge to the performance and reliability of ONNs. Against this backdrop, the exploration of neural network technologies based on partially coherent light (PCL) becomes crucial, underscoring the urgent need for efficient and convenient methods to generate, calibrate, and precisely control PCL. Herein, we propose an integrated ONNs input system incorporating the functions of generation, calibration, precise control, and image reconstruction of PCL. The system not only enables the precise conversion and calibration from lasers to specific coherence levels but also achieves high-repetition-rate, high-quality image reconstruction. Consequently, it rapidly provides abundant high-quality samples support for the input layer of ONNs, significantly enhancing training efficiency. The system focuses on the future recognition and analysis of large, high-resolution images by ONNs, demonstrating robust scalability and further emphasizing the advantages of ONNs as highly efficient parallel systems. Our research effectively addresses the challenges posed by laser coherence degradation, offering new insights into the development and practical implementation of ONNs. Furthermore, it paves the way for their seamless integration into fields such as aerospace remote sensing and image classification recognition.

关键词： Light field imaging Optical reconstruction Partially coherent light All-optical computing

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A novel architecture for automated delineation of the agricultural fields using partial training data in remote sensing images

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 234卷

作者： Sumesh, K. C. Aryal, Jagannath Ryu, Dongryeol Univ Melbourne Dept Infrastruct Engn Earth Observat & AI Res Grp Melbourne Vic 3010 Australia Univ Melbourne Dept Infrastruct Engn Melbourne Environm Sensing & Modelling Lab Melbourne Vic 3010 Australia

Digital agricultural services (DAS) rely on timely and accurate spatial information of agricultural fields. Initiatives, including deep learning (DL), have been used to extract accurate spatial information using remote sensing images. However, DL approaches require a large amount of fully segmented and labelled field boundary data for training that is not readily available. Obtaining high-quality training data is often costly and timeconsuming. To address this challenge, we develop a multi-scale, multi-task DL-based novel architecture with two modules, an edge enhancement block (EEB) and a spatial attention block (SAB), using partial training data (i.e., weak supervision). This architecture is capable of delineating narrow and weak boundaries of agricultural fields. The model simultaneously learns three tasks: boundary prediction, extent prediction and distance estimation, and enhances the generalisability of the network. The EEB module extracts semantic edge features at multiple levels. The SAB module integrates the features from the encoder and decoder to improve the geometric accuracy of field boundary delineation. We conduct an experiment in Ille-et-Vilaine, France, using time-series monthly composite images from Sentinel-2 to capture key phenological stages of crops. The segmentation output from different months is combined and post-processed to generate individual field instances using hierarchical watershed segmentation. The performance of our method is superior in both pixel-based (86.42% Matthew's correlation coefficient (MCC)) and object-based accuracy measures (76% shape similarity and 60% intersection over union (IoU)) to existing multi-task models. The ablation study shows that the EEB and SAB modules enhance the efficiency of feature extraction relevant to field extent and boundaries and improve accuracy. We conclude that the developed model and method can be used to improve the extraction of agricultural fields under weak supervision and different set

关键词： Field boundary delineation remote sensing Segmentation Deep learning Weak supervision

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Cloud and Cloud Shadow Detection for Multi-Modal Imagery With Gap-Filling Applications

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IEEE ACCESS 2025年 13卷 7396-7406页

作者： Cho, Keunhoo Park, Seongwook Seong, Boram Lee, Seongwhan Park, Jae-Pil Nara Space Technol Inc Seoul 07245 South Korea

Cloud and cloud shadow (CCS) detection algorithms play a crucial role in the preprocessing of remote sensing data and directly affect the accuracy of subsequent analyses, making them an essential step in most analytical processes. Recent techniques for detecting CCS often employ deep learning methods, which are effective but typically require extensive training data specific to each type of satellite imagery. This study presents a new methodology that applies a model trained on the preconstructed KOMPSAT-3/3A CCS dataset to Landsat-8 and Sentinel-2 satellite imagery. The experimental results demonstrated that the CCS detection model based on KOMPSAT-3/3A achieved a mean F1 score of 0.846 on the test dataset. When applied to Landsat-8 SPARCS and Sentinel-2 CloudSEN12 test datasets, it also showed high performance, with mean F1 scores of 0.741 and 0.8, respectively, effectively indicating that multi-modal CCS detection can be successfully implemented. Applying this model to different sensor imagery confirmed its effectiveness in gap filling, which can be utilized to enhance time-series analyses where continuous monitoring is required. In conclusion, this approach not only proves beneficial for time-series analysis but also significantly reduces the time and effort required to build datasets in deep learning-based CCS detection.

关键词： Transformers remote sensing Earth Satellite images Artificial satellites Cloud computing Training Spatial resolution Computational modeling Deep learning Clouds cloud shadows multi-modal imagery KOMPSAT-3 KOMPSAT-3A Sentinel-2 Landsat-8 swin transformer

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QAGA-Net: enhanced vision transformer-based object detection for remote sensing images

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INTERNATIONAL JOURNAL OF INTELLIGENT computing AND CYBERNETICS 2025年第1期18卷 133-152页

作者： Song, Huaxiang Xia, Hanjun Wang, Wenhui Zhou, Yang Liu, Wanbo Liu, Qun Liu, Jinling Hunan Univ Arts & Sci Sch Geog Sci & Tourism Changde Peoples R China

Purpose - Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it. Design/methodology/approach - To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model's training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi- scale information fusion, and an efficient pooling (EP) module to optimize the model's capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy. Findings - We verified the performance of QAGA-Net by using two different efficient ViT models as the detector's backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGANet achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively. Originality/value - This paper highlights the impact of sparse data distribution

关键词： QAGA-Net Vision transformer Object detection remote sensing image Deep learning

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