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Conference on Earth Resources and Environmental Remote Sensing/GIS Applications IV

作者： Vaiopoulos, Aristides D. Nikolakopoulos, Konstantinos G. Univ Athens Dept Geol GR-10679 Athens Greece

ISBN: (纸本)9780819497635

In this study ten commercial available fusion techniques and more especially the Ehlers, Gram-Schmidt, High Pass Filter, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Modified IHS (ModIHS), Pansharp, PCA, HCS (Hyperspherical Color Space) and Wavelet were used for the fusion of Geoeye panchromatic and multispectral data. The panchromatic data have a spatial resolution of 0.5m while the multispectral data have a spatial resolution of 2.0m. The optical result, the statistical parameters and different quality indexes such as ERGAS, Q, entropy were examined and the results are presented. The broader area of Pendeli mountain near to the city of Athens Greece and more especially two sub areas with different characteristics were chosen for the comparison. The first sub area is located at the edge of the urban fabric and combines at the same time the characteristics of an urban and a rural area. The second sub area comprises a large open quarry and it is suitable to examine which fused product is more suitable for mine monitoring.

关键词： Geoeye fusion algorithms quality control

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ACTIVE AND PASSIVE L-BAND MICROWAVE REMOTE SENSING FOR SOIL MOISTURE -A TEST-BED FOR SMAP fusion algorithms

ACTIVE AND PASSIVE L-BAND MICROWAVE REMOTE SENSING FOR SOIL ...

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IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

作者： Montzka, C. Bogena, H. Jagdhuber, T. Hajnsek, I. Horn, R. Reigber, A. Hasan, S. Ruediger, C. Jaeger, M. Vereecken, H. Res Ctr Julich Inst Bio & Geosci Agrosphere IBG 3 Julich Germany

ISBN: (纸本)9781479957750

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture and its freeze/thaw state. The SMAP measurement approach is to integrate L-band radar and radiometer as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments will be a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were flown on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e. all types of land cover and experimental monitoring sites. The obtained data sets are used as a test-bed for the analysis of existing and development of new active-passive fusion techniques.

关键词： SMAP Airborne Microwave Remote Sensing fusion algorithms Soil Moisture

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EVALUATION AND ANALYSIS OF fusion algorithms FOR ACTIVE AND PASSIVE REMOTE SENSING IMAGE

EVALUATION AND ANALYSIS OF FUSION ALGORITHMS FOR ACTIVE AND ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Gamba, Paolo Liu, Pei Du, Peijun Lin, Hui Univ Pavia Dipartimento Elettron I-27100 Pavia Italy

ISBN: (纸本)9781467311595

In order to compare fusion algorithms considering both active and passive remotely sensed data, a few well-known techniques, including Brovey, Gram-Schmidt spectral sharpening (GS), Hue Saturation Value (HSV), Principal Component Analysis spectral sharpening (PCA), and a trous wavelet transform applied in the Hue Intensity Saturation space (ATWT+HIS) are compared with the simple joint analysis of the original SAR and optical images. Experiments are performed using pairs of ALOS ANVIR-2 and PALSAR, SPOT and PALSAR, Landsat TM and ERS data. In this paper, the above mentioned methods and dataset combinations are tested and compared by means of quantitative indexes such as entropy, average gradient (AG), correlation coefficient (CC), deviation index (DI) and classification accuracy. The results obtained demonstrate that classification accuracy values can be improved by using these approaches by as much as 10% with respect to the best achievable value using only optical and SAR data separately. By means of a detailed analysis of the relationship between classification accuracy and quantitative indexes usually considered to evaluate the value of the fused products, our experiments show that larger deviation index (DI) and smaller correlation coefficient (CC) values are usually connected to more accurate classification results.

关键词： fusion algorithms Active and passive remotely sensed data

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EVALUATION AND ANALYSIS OF fusion algorithms FOR ACTIVE AND PASSIVE REMOTE SENSING IMAGE

EVALUATION AND ANALYSIS OF FUSION ALGORITHMS FOR ACTIVE AND ...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Paolo Gamba Pei Liu Peijun Du Hui Lin Dipartimento di Elettronica Univ. di Pavia Key Laboratory for Land Environment and Disaster Monitoring of SBSM China University of Mining and Technology Department of Geoinformatics Nanjing Univ. School of Geodesy and Geomatics Xuzhou Normal Univ.

ISBN: (纸本)9781467311601

关键词： fusion algorithms Active and passive remotely sensed data

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Multisensor fusion and integration: algorithms, applications, and future research directions

Multisensor fusion and integration: Algorithms, applications...

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IEEE International Conference on Mechatronics and Automation

作者： Luo, Ren C. Chou, Ying Chih Chen, Ogst Univ Chung Chang Dept Elect Engn Chiayi Taiwan

ISBN: (纸本)9781424408276

Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or, more timely information in a system can provide more reliable and accurate information. This paper provides an overview of current sensor technologies and describes the paradigm of multisensor fusion algorithms and applications of multisensor fusion in localization and tracking, robotics, identification and classification, vehicle sensing, and so on. Finally, future research directions of multisensor fusion technologies including microsensors, smart sensors, and adaptive fusion techniques are presented.

关键词： classification of sensors fusion algorithms multisensor fusion multisensor integration

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Advances in DeepFake detection algorithms: Exploring fusion techniques in single and multi-modal approach

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INFORMATION fusion 2025年 118卷

作者： Kumar, Ashish Singh, Divya Jain, Rachna Jain, Deepak Kumar Gan, Chenquan Zhao, Xudong Bennett Univ Sch Comp Sci Engn & Technol Greater Noida Uttar Pradesh India Bhagwan Parshuram Inst Technol New Delhi India Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Symbiosis Int Univ Symbiosis Inst Technol Pune 412115 India Chongqing Univ Posts & Telecommun Sch Cyber Secur & Informat Law Chongqing 400065 Peoples R China

In recent years, generative artificial intelligence has gained momentum and created extremely realistic synthetic multimedia content that can spread misinformation and mislead society. Deepfake detection is a technique consisting of frameworks, algorithms and approaches to predict manipulated contents namely, image, audio and video. To this end, we have analyzed and explored various deepfake detection frameworks by categorizing them as single-modal or multi-modal approaches. To provide better understanding and clarity, single-modal approaches are further categorized as conventional and advanced techniques. Conventional techniques extract complementary handcrafted features and classify them using machine-learning-based algorithms. On the other hand, advanced techniques adopt deep learning and hybrid algorithms to detect deepfakes. Multi-modal techniques utilize a mixture of two or more modalities for feature extraction and fuse them to obtain the final classification scores. These techniques are also categorized either as deep learning or hybrid techniques. The complementary features, multiple modalities, and deep learning models are fused adaptively using score-level or feature-level fusion. The advantages, features, practical applications, and limitations under each category are highlighted to address the challenges and determine future trends to counter deepfakes. In addition, recommendations are also elaborated to evaluate the potential of artificial intelligence in deepfake detection for providing a safer and more reliable digital world.

关键词： DeepFake Artificial intelligence Generative adversarial network fusion algorithms Transformer Detection

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SC-MLIDS: fusion-based Machine Learning Framework for Intrusion Detection in Wireless Sensor Networks

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AD HOC NETWORKS 2025年 175卷

作者： Zhang, Hongwei Upadhyay, Darshana Zaman, Marzia Jain, Achin Sampalli, Srinivas Dalhousie Univ Fac Comp Sci Halifax NS B3H 4R2 Canada Cistel Technol Inc Res & Dev Dept Ottawa ON K2E 7V7 Canada Norleaf Networks Res & Dev Dept Gatineau PQ J8Y 2V5 Canada

This paper proposes the Server-Client Machine Learning Intrusion Detection System (SC-MLIDS), a novel fusion framework designed to enhance security in Wireless Sensor Networks (WSNs), which are inherently vulnerable to various security threats due to their distributed nature and resource constraints. Traditional Intrusion Detection Systems (IDSs) often face challenges with high computational demands and privacy issues. SC-MLIDS addresses these problems by integrating Federated Learning (FL) with a multi-sensor fusion approach to implementing two layers of defence that operate independently of specific attack types. Moreover, this framework leverages a server-client architecture to efficiently manage and process data from sensor nodes, sink nodes, and gateways within the network. The core innovation of SC-MLIDS lies in its dual model aggregation algorithms at the gateway: one assesses model performance and weight, while the other uses majority voting to integrate predictions from both client and server models. As a result, this approach reduces redundant data transmissions and enhances detection accuracy, making it more effective than conventional methods in WSNs. Our proposed framework outperforms current state-of-the-art techniques, achieving F1-scores of 99.78% and 98.80% for the two aggregation algorithms, namely, Weighted Score and Majority Voting. This validation demonstrates the effectiveness of SC-MLIDS in providing accurate intrusion detection and robust data management.

关键词： Wireless sensor networks Machine learning Intrusion detection systems Ensemble learning Federated learning Server-client models Model aggregation Network security fusion algorithms

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Structural damage detection by integrating data fusion and probabilistic neural network

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ADVANCES IN STRUCTURAL ENGINEERING 2006年第4期9卷 445-458页

作者： Jiang, Shao-Fei Zhang, Chun-Ming Koh, C. G. Shenyang Jianzhu Univ Sch Civil Engn Shenyang 110168 Liaoning Peoples R China Northeastern Univ Sch Resources & Civil Engn Shenyang 110004 Liaoning Peoples R China Natl Univ Singapore Dept Civil Engn Singapore 117576 Singapore

Over the past two decades, multi-sensor data fusion method has attracted increasing attention to structural health monitoring due to its inherent capabilities in extracting information from different sources and integrating them into a consistent, accurate and intelligible data set. Meanwhile, since the probabilistic neural network (PNN) describes measurement data in a Bayesian probabilistic approach, it has been successfully applied to structural damage detection (Jiang et al. 2004;Klenke et al. 1996;Ko et al. 1999;Ni et al. 2001). In order to make full use of multi-sensor data (or information) from multi-resource and to improve the diagnosis accuracy of the health conditions for complex structures, it is advisable to combine these methods and exploit their individual advantages. In this paper, a 5-phase complex structural damage detection method by integrating data fusion and PNN is developed and implemented. The proposed method is then applied to damage detection and identification of two simulation examples. The result shows that the proposed method is feasible and effective for damage identification.

关键词： data fusion damage detection wavelet analysis energy feature fusion algorithms probabilistic neural network

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Multiresolution Compressive Feature fusion for Spectral Image Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第12期57卷 9900-9911页

作者： Marcos Ramirez, Juan Arguello, Henry Univ Los Andes Dept Elect Engn Merida 5101 Venezuela Univ Ind Santander Dept Comp Sci Bucaramanga 680002 Colombia

Compressive spectral imaging (CSI) has emerged as an alternative acquisition framework that simultaneously senses and compresses spectral images. In this context, the spectral image classification from CSI compressive measurements has become a challenging task since the feature extraction stage usually requires reconstructing the spectral image. Moreover, most approaches do not consider multi-sensor compressive measurements. In this paper, an approach for fusing features obtained from multi-sensor compressive measurements is proposed for spectral image classification. To this end, linear models describing low-resolution features as degraded versions of the high-resolution features are developed. Furthermore, an inverse problem is formulated aiming at estimating high-resolution features including both a sparsity-inducing term and a total variation (TV) regularization term to exploit the correlation between neighboring pixels of the spectral image, and therefore, to improve the performance of pixel-based classifiers. An algorithm based on the alternating direction method of multipliers (ADMM) is described for solving the fusion problem. The proposed feature fusion approach is tested for two CSI architectures: three-dimensional coded aperture snapshot spectral imaging (3D-CASSI) and colored CASSI (C-CASSI). Extensive simulations on various spectral image data sets show that the proposed approach outperforms other classification approaches under different performance criteria.

关键词： Feature extraction Image coding Apertures Sensors Optical imaging Image resolution Compressive spectral imaging (CSI) feature fusion fusion algorithms multi-sensor spectral image classification

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A sensor data fusion algorithm based on suboptimal network powered deep learning

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ALEXANDRIA ENGINEERING JOURNAL 2022年第9期61卷 7129-7139页

作者： Pan, Ning Zhengzhou Tourism Coll Sch Informat Engn Zhengzhou 450000 Henan Peoples R China

To improve the performance of data fusion in wireless sensor networks, based on the suboptimal network powered deep learning model, a data fusion algorithm that combines a stacked autoencoder (SAE) and a clustering protocol is proposed. The fusion algorithm for sensor data is investigated by suboptimal network powered deep learning to analyse the fusion processing at the sensor measurement level and the fusion processing at the localization result level. This paper establishes a suboptimal network powered deep learning model and derives its weighted least squares solution algorithm based on this model, also, the best linear unbiased estimation method for fusion of vibration sensor and vision sensor localization results is adopted. First, to overcome the filter divergence caused by rounding error, reduce the computational complexity, ensure the nonnegativity of the covariance, and improve the convergence speed of the filter, the target state volume after fusion using the proposed model is closer to the real state volume of the target compared to the state volume of each local sensor detection, verify the validity of the algorithm was increased. (c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://***/

关键词： Suboptimal networks Deep learning Sensor data fusion algorithms

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