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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing XXIX"

共 3583 条记录，以下是371-380 订阅

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Hybrid Watermarking Algorithm to Protect and Authenticate KhalifaSat imagery using DWT-SVD and SHA3 Hash Key 5

Hybrid Watermarking Algorithm to Protect and Authenticate Kh...

引用

5th IEEE International conference on image processing Applications and Systems (IPAS)

作者： Panthakkan, Alavikunhu Anzar, S. M. Al Mansoori, Saeed Al-Ahmad, Hussain Univ Dubai Coll Engn & IT Dubai U Arab Emirates TKM Coll Engn Dept Elect & Commun Kollam Kerala India Mohammed Bin Rashid Space Ctr Applicat Dev Sect Dubai U Arab Emirates

ISBN: (纸本)9781665462198

Using DWT-SVD and SHA3 Hash function, this research aims to develop an ownership protection and image authentication technique that embeds the watermark information and hash authentication key in a hybrid domain. The experiment was conducted with multispectral images from the KhalifaSat. The Performance of the proposed method is evaluated using wavelet domain signal to noise ratio (WSNR), structural similarity index measurement (SSIM) and peak signal to noise ratio (PSNR). To analyse the efficacy of the recovered watermark, two metrics are used: Normalized Correlation (NC) and image Quality Index (IQI). The method presented is robust against many intended and unintended attacks. Without sacrificing transparency, our proposed watermarking approach meets the objectives of imperceptibility and robustness. It accurately detects the manipulated locations on the satellite image and is sensitive to even small changes.

关键词： Digital watermarking Hash Function image Authentication Ownership Protection

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Human Cognition Based Models for Natural and remote sensing image Analysis 4th

Human Cognition Based Models for Natural and Remote Sensing ...

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4th International conference on Machine Intelligence and signal processing, MISP 2022

作者： Chandra, Naveen Vaidya, Himadri Wadia Institute of Himalayan Geology Dehradun India Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819900466

Cognitive science is the study of the human mind and interrelated processes. It concentrates on how information about the world around us is processed within the nervous system. The objective of this study is to illuminate the cognitive aspects of image interpretation and image analysis. Cognitive tasks defined in this study are perception, attention, and reasoning which are used by the human cognition system to interpret the world around us. Cognitive models developed in the past are reviewed in this paper which mainly focuses on image interpretation mechanisms using different cognitive capacities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image analysis

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signal-Centric remote sensing via Alternative Preprocessing and Acoustic processing for ML-Driven Applications

Signal-Centric Remote Sensing via Alternative Preprocessing ...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Luna, Logan Jansen-Sánchez, Sirio Demirkiran, Ilteris Ghelarducci, Leo Embry-Riddle Aeronautical University Daytona Beach United States

ISBN: (纸本)9798331504847

The dominant method of processing sonar data is using image-based representations, requiring the preprocessing of image data on autonomous systems. We propose an alternative data processing method for remote sensing applications via the use of data in Comma-Seperated Value format. Experimentation on our alternative approach shows a reduction of processing time by 91.18%, an improvement in accurate object detection by Machine Learning, and an increase in SNR (signal-to-noise ratio), PSNR (Peak signal-to-noise ratio), and other evaluation metrics. © 2025 IEEE.

关键词： Data curation

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FAST FUSION OF HYPERSPECTRAL AND MULTISPECTRAL imageS : A TUCKER APPROXIMATION APPROACH 29

FAST FUSION OF HYPERSPECTRAL AND MULTISPECTRAL IMAGES : A TU...

引用

IEEE International conference on image processing (ICIP)

作者： Prevost, C. Chainais, P. Boyer, R. Univ Lille CNRS Cent Lille UMR 9189 CRIStAL F-59000 Lille France

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Hyperspectral super-resolution based on coupled Tucker decomposition has been recently considered in the remote sensing community. The state-of-the-art approaches did not fully exploit the coupling of information contained in hyperspectral and multispectral images of the same scene. This paper proposes a new algorithm that overcomes this limitation. It accounts for both the high-resolution and the low-resolution information in the model by solving a set of least-squares problems. In addition, we provide exact recovery conditions for the super-resolution image in the noiseless case. Our simulations show that the proposed algorithm achieves very good reconstruction quality with a very low computational complexity.

关键词： Couplings Runtime image color analysis Computational modeling Superresolution Lakes Approximation algorithms

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image reconstruction and enhancement for radar imaging using IR-UWB signals 7

Image reconstruction and enhancement for radar imaging using...

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7th International conference on image and signal processing and their Applications, ISPA 2022

作者： Abadlia, Hamza Mesloub, Ammar Kakouche, Ibrahim Sadoudi, Said Ecole Militaire Polytechnique Laboratoire Telecommunications Bordj el Bahri Alger16111 Algeria Ecole Militaire Polytechnique Laboratoire Traitement du Signal Bordj el Bahri Alger16111 Algeria Ecole Militaire Polytechnique Laboratoire Systèmes Électroniques et Numériques Bordj el Bahri Alger16111 Algeria

ISBN: (纸本)9781665480420

Impulse response ultra-wideband (IR-UWB) radar has many applications for remote sensing of individuals, as well as imaging of subjects behind a wall. In the present paper, we first introduce the theory behind the IR-UWB radar for through-the-wall imaging (TWI). Then, we present the system realization that combines a single radar with an adequate rail displacement mechanism. Finally, we depict the real-life image reconstruction using spatial filtering followed by spectral processing of the synthetic aperture data for signal enhancement. Experimental results demonstrate the effectiveness of the IR-UWB radar and the introduced processing technique. © 2022 IEEE.

关键词： Synthetic aperture radar

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Effects of Drifting Ionospheric Irregularities for L-Band Geosynchronous SAR Imaging Simulation 8

Effects of Drifting Ionospheric Irregularities for L-Band Ge...

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8th International conference on signal and image processing, ICSIP 2023

作者： Tang, Feixiang Ji, Yifei Zhang, Yongsheng Dong, Zhen Gao, Heli Zhao, Bingji Zhang, Qingjun National University of Defense Technology College of Electronic Science and Technology Changsha China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (纸本)9798350397932

The L-band geosynchronous synthetic aperture radar (GEO SAR) is susceptible to the ionospheric irregularities due to its lower carrier frequency, which can be more complicated because the velocity of drifting irregularities is comparable with the scanning velocity of ionospheric penetration points (IPPs) of GEO SAR. In this paper, a drifting ionospheric scintillation simulator (DISS) is proposed for generating L-band GEO SAR data affected by the drifting ionospheric irregularities. The DISS is divided into a static 2D scintillation transfer function generator, a drifting IPP offset indexer and a GEO SAR echo simulator. Based on the DISS, the simulation results indicate that the drifting ionospheric irregularities of the L-band GEO SAR will present different artifacts and more serious decorrelations in L-band GEO SAR images, compared with the L-band low-Earth-orbit (LEO) SAR. © 2023 IEEE.

关键词： Ionosphere

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Implementing a Deep Learning Based Bridge Defect Detection System Using Jetson Nano 2

Implementing a Deep Learning Based Bridge Defect Detection S...

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2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Senapati, Ranjan K. Kumar, K. Akhil Prasad, P.M.K. VNR VJIET Dept. of ECE Hyderabad India GVPCE for Women Dept. of ECE Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350366570

This work describes the creation and application of an NVIDIA Jetson Nano platform-based deep learning bridge fault detection system. Bridges constitute crucial pieces of infrastructure, and maintaining safety and averting catastrophic failures depend on quick detection of flaws like corrosion, cracks, or structural damage. Conventional inspection techniques can be time-consuming, labor-intensive, and prone to mistakes. Here, we offer a computer vision-based methodology that uses deep learning techniques to automatically identify and categorize bridge structure faults from photos collected with fixed cameras or by drones. Real-time defect detection uses the NVIDIA Jetson Nano as an edge computing device because of its great computational capacity and low power consumption. The three primary phases of the suggested system include preprocessing images, employing convolutional neural networks (CNNs) for fault identification, and post-processing for analysis and visualization. Using the Jetson Nano's GPU acceleration for quick inference, we gathered a dataset of bridge photos with different kinds of flaws and carried out comprehensive tests to assess the system's performance. The suggested method shows efficient covering defects with an accuracy of 96%, while preserving the Jetson Nano platform's real-time processing capabilities. © 2024 IEEE.

关键词： image segmentation

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Design and Implementation of Spaceborne SAR Radio Frequency Interference Detection and Suppression System

Design and Implementation of Spaceborne SAR Radio Frequency ...

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2023 International conference on remote sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Hu, Xingwang Zhang, Hengrui Li, Ning Guo, Zhengwei School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China

ISBN: (纸本)9781510668898

Spaceborne synthetic aperture radar (SAR) is an active radar system carried on a satellite, with the help of synthetic aperture technology to achieve all-day, all-weather, high-resolution imaging, it has been widely used in military reconnaissance, resource exploration, deep space exploration and other fields. As the electromagnetic environment becomes more and more complex, spaceborne SAR is more likely to receive radio frequency interference (RFI) from other radar equipment in the working frequency band, which will affect the interpretation of SAR images and lead to image quality degradation. Although there are endless methods for RFI detection and suppression, each method has its limitations and can only deal with specific interference scenarios. A unified, robust and high-precision SAR interference detection and suppression platform is urgently needed. Based on SAR signal processing technology, this paper uses multiple methods to jointly design a unified high-robust system for SAR data containing different types of RFI, and realizes efficient detection and suppression of spaceborne SAR RFI. The popularization and application of this system is of great significance to improve the anti-interference ability of SAR system, improve the processing efficiency of a large number of SAR data in complex scenes and ensure the accuracy of data. © 2023 SPIE.

关键词： Synthetic aperture radar

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Advances in the Hyperspectral imager ANFA and It Is Role in the Colombian LEOPAR Satellite Mission

Advances in the Hyperspectral Imager ANFA and It Is Role in ...

引用

IEEE Aerospace conference

作者： Rojas-Rodriguez, Leandro Rodriguez-Ferreira, Julian Salgado-Meza, Pedro A. Rincon, Sonia Acero, Ignacio F. Hernandez, Francisco L. Palencia-Solano, Francisco Arciniegas-Arbelaez, Daniel Univ Ind Santander E3T Bucaramanga 680002 Colombia Fuerza Aeroespacial Colombiana CITAE Cali 760011 Colombia USA Bogota 111711 Colombia Univ Valle EIEE Cali 760042 Colombia

ISBN: (纸本)9798350304626

Colombia is an emerging space nation currently transitioning from being an operator of space systems to becoming a satellite manufacturer. The LEOPAR mission represents Colombia's fourth satellite development endeavor. It entails a 3U CubeSat platform equipped with a hyperspectral camera known as ANFA, designed for remote sensing in the spectral range of 450 to 900 nm. Its primary objective is to identify vegetation and deforested areas across Colombian territory. The design and implementation of ANFA, in its initial phase as a laboratory prototype, underwent multiple iterations to align with mission requirements based on the state of the art. Rigorous laboratory tests have successfully validated the payload's proper operation in terms of optical, mechanical, and electronic aspects of operational concepts. The ANFA laboratory prototype was developed using Commercial Off-The-Shelf (COTS) devices to emulate the optical subsystem and integrate it with the electronic subsystem. This prototype achieved scene capture, data transmission through the SPI protocol to the instrument's main microcontroller, data processing, storage in an SD memory card, and image reconstruction to identify spectral signatures across each pixel in the hyperspectral data cube. Simultaneously, significant progress has been made in designing the optoelectronic detection chain for ANFA, based on the development of a radiometric model for analyzing a reference scene and transforming scene reflectance to Top of the Atmosphere (TOA). Subsequently, we plan to calculate the signal-to-Noise Ratio (SNR) performance parameter as a tool to select a suitable detector for our hyperspectral camera, meeting the requirements of the LEOPAR mission. Once this process is completed, the goal is to scale the prototype to an engineering model and, ultimately, a flight model. The vision is for ANFA to become the first Colombian hyperspectral instrument launched into space, thereby aligning this development with ongoing

关键词： image reconstruction

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Semantic Segmentation for Multi-Scene remote sensing images with Noisy Labels Based on Uncertainty Perception

Semantic Segmentation for Multi-Scene Remote Sensing Images ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Xinran Lyu Libao Zhang School of Artificial Intelligence Beijing Normal University Beijing China

As the annotation of remote sensing images requires domain expertise, it is difficult to construct a large-scale and accurate annotated dataset. image-level annotation data learning has become a research hotspot. In addition, due to the difficulty in avoiding mislabeling, label noise cleaning is also a concern. In this paper, a semantic segmentation method for remote sensing images based on uncertainty perception with noisy labels is proposed. The main contributions are three-fold. First, a label cleaning method based on iterative learning is presented to handle noise labels such as missing or incorrect annotations. Second, a two-stage semantic segmentation model is proposed for image-level annotation, which eliminates the need for post-processing steps during testing. Lastly, a complementary uncertainty perception function is introduced to improve the utilization of dataset features and enhance the accuracy of segmentation. The effectiveness of this method was verified through comprehensive evaluation with 7 models on four datasets.

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