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Application of Frequency Domain Filtering in Edge Detection 10th

Application of Frequency Domain Filtering in Edge Detection

引用

10th International conference on signal and Information processing, Network and Computers, ICSINC 2022

作者： Li, Bo He, Hongyan Beijing Institute of Space Mechanics and Electricity Beijing100094 China Key Laboratory for Advanced Optical Remote Sensing Technology of Beijing Beijing100094 China

ISBN: (纸本)9789811999673

image edge extraction is a widely studied problem in the field of digital image processing. When performing edge extraction on noisy images, in order to avoid that the spatial domain filtering cannot preserve the edge information to the greatest extent while suppressing the noise, the frequency domain band-stop filter is used instead of the spatial domain low-pass filter to preprocess the image. In this way, part of the high-frequency information generated by the edge is preserved, and the accuracy of the detection result is improved. Based on standard frequency domain band-stop filter, a special filter is constructed for the edge in a specific direction to further improve the edge detection quality in this direction. In addition, in the edge extraction algorithm, the double-threshold Otsu adaptive threshold detection algorithm is integrated into the Canny algorithm in the step of edge selection. Experiments show that frequency domain filtering can better detect edges and is more convenient to design specific filters for specific problems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge detection

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Minimum-Volume Non-Negative Block-Term Decomposition: Blind Data Fusion and Unmixing with Estimation of the Number of Endmembers

Minimum-Volume Non-Negative Block-Term Decomposition: Blind ...

引用

European signal processing conference (EUSIPCO)

作者： Clémence Prévost Valentin Leplat Univ. Lille CNRS Centrale Lille Lille France Innopolis University Russia

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper introduces a family of efficient algorithms for joint super-resolution and unmixing in remote sensing. They utilize coupled tensor decompositions with minimum-volume regularizations and incorporate the $\beta$ -divergence to handle diverse noise statistics, while estimating degradation operators mapping the unknown super-resolution image to the degraded observations. They aid in identifying factors in the tensor decomposition and managing rank deficiencies effectively. These algorithms show competitive performance compared to existing methods, even in challenging scenarios: unknown degra-dation operators and estimating the number of endmembers.

关键词： Tensors Superresolution Noise Mathematical analysis signal processing algorithms Europe Noise measurement remote sensing image reconstruction Optimization

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Generalized Pareto distribution exploited for ship detection as a model for sea clutter in a Pol-SAR application 7

Generalized Pareto distribution exploited for ship detection...

引用

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

作者： Mahgoun, Hichem Taieb, Ali Azmedroub, Boussad Souissi, Boularbah Ecole Nationale Supérieure Maritime Département des Sciences Maritimes des Transports et des Activités Portuaires Tipaza Algeria Université Blida 1 Institut de l'Aéronotique et des Études Spatiales Blida Algeria Université des Sciences et de la Technologie Houari-Boumédiène Departement Télécommunication Alger Algeria

ISBN: (纸本)9781665480420

Ship remote sensing can be regarded as an essential tool exploited for the purpose of managing maritime traffic. It can contribute promptly in the monitoring network system by increasing ships safety at sea. This work aims to verifier a new model for sea clutter based on analytic distribution. Our objective lies in integrating it to develop a constant false alarm procedure for ship detection. Generalized Pareto distribution was exploited for this objective on Polarimetric-SAR images. We were able in this paper to compute the detector based on the aforementioned distribution and assess the performances related to ship detection capabilities. The set of data processed in this paper was acquired by RADARSAT-2 satellite over a port area of the province of Vancouver located in the coastline region of Canada. It was verified in this paper that the Generalized Pareto distribution is a valid model of sea reflectivity. Second, the derived procedure of ship detection related to this stochastic process achieves a high detection probability (93%) for the HH polarization while showing a small level of false alarm (0.5%). © 2022 IEEE.

关键词： remote sensing

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PREDICTING SOIL PROPERTIES FROM HYPERSPECTRAL SATELLITE imageS 29

PREDICTING SOIL PROPERTIES FROM HYPERSPECTRAL SATELLITE IMAG...

引用

IEEE International conference on image processing (ICIP)

作者： Kuzu, Ridvan Salih Albrecht, Frauke Arnold, Caroline Kamath, Roshni Konen, Kai Helmholtz Artificial Intelligence Cooperat Unit Munich Germany German Climate Comp Ctr DKRZ Hamburg Germany Forschungszentrum Julich GmbH Julich Supercomputing Ctr Julich Germany German Aerosp Ctr DLR Inst Software Technol Cologne Germany German Aerosp Ctr DLR Remote Sensing Technol Inst IMF Cologne Germany

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

The AI4EO HYPERVIEW challenge seeks machine learning methods that predict agriculturally relevant soil parameters (K, Mg, P2O5, pH) from airborne hyperspectral images. We present a hybrid model fusing Random Forest and K-nearest neighbor regressors that exploit the average spectral reflectance, as well as derived features such as gradients, wavelet coefficients, and Fourier transforms. The solution is computationally lightweight and improves upon the challenge baseline by 21.9%, with the first place on the public leader-board. In addition, we discuss neural network architectures and potential future improvements.

关键词： hyperspectral images random forests artificial neural networks soil parameter estimation regression

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remote sensing Small Target Detection Research Based on Improved YOLOv7

Remote Sensing Small Target Detection Research Based on Impr...

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signal processing and Intelligent Computing (SPIC), International conference on

作者： Jia Guo Xiannan Wang Meihua Song Shuang Zhao Cean Guo Jian Zhang Hao Cheng College of Equipment Engineering Shenyang Ligong University Shenyang China Key Laboratory of Safe Mining of Deep Metal Mines Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

This study introduces a streamlined improvement algorithm based on YOLOv7 to address the challenges of multiple small targets, complex backgrounds, limited feature points, and environmental influences in remote sensing images. The algorithm incorporates the GSConv module and upsampling to achieve more effective feature fusion. The GSConv module combined with the VoV-GSCSP module achieves Slim-Neck for reducing algorithm complexity and improving accuracy. The Receptive Field Enhancement (RFE) module is introduced after the spatial pyramid pooling layer (SPPCSPC) to enhance the receptive field of small targets. By changing the settings of convolution operations and increasing the receptive field size of each convolutional layer, local and global features in the image are better captured. Information flow is maintained through skip connections. Experimental findings show that the enhanced YOLOv7 algorithm reaches an mAP of 95.5% on the NWPU VHR-10 dataset, marking a 6.4% improvement over the original algorithm’s mAP. That improves the accuracy of object detection in remote sensing images.

关键词： Location awareness Accuracy image recognition Target recognition Convolution signal processing algorithms Object detection Streaming media Complexity theory remote sensing

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Urban Change Detection and Analysis of Symbiosis University Nagpur, India Based on remote sensing Techniques 3

Urban Change Detection and Analysis of Symbiosis University ...

引用

3rd International conference on signal processing and Communication (ICPSC)

作者： Gaidhane, Pritesh D. Dorge, Prabhakar D. Balamwar, Sanjay V. Yeshwantrao Chavan Coll Engn Dept Elect & Telecommun Engn Nagpur Maharashtra India Applicat Ctr Dept Maharashtra Remote Sensing Nagpur Maharashtra India

ISBN: (纸本)9781665428644

remote sensing is basically the method of detection of the physical parameters of a specific area along with that it also monitors the area by measuring the radiation parameters from satellite. The cameras are designed to gather remotely sensed images, which are used to sense things of the earth's surface. Like the cameras on the satellites and the airplanes takes pictures of the different area, allowing us to get every minute detail about it. Now, change detection is the process to identify the changes or differences in various parts of land characteristics at different intervals. This process can be carried by manual observation or by the use of remote sensing software. Because of the rapid urbanization there has been a significant impact on resources and urban environment. But with the use of high quality multi-spatial and multi-temporal remote sensing data, it is quite easy to possibly observe the changes in the urban environment. Therefore, the given study aims to quantify changes in the urban area of Symbiosis University Nagpur, Maharashtra India using Land satellite image. These changes of urbanization are detected by satellite images of Land-Sat MSS in march-2016, march-2018 and may-2020 using a geographic information system (GIS). The change of urbanization in Symbiosis University is for building, road, garden and ground are 17.35%, 20.71%, 1.65% and 0.89% respectively and the total change is 43.51%. From this change we will receive the taxes as per its ongoing construction and it will be easy for government.

关键词： Mobility patterns K-means GIS-image resolution digital image processing remote sensing image classification image thresholding mapping of images etc.

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Multi-Source remote sensing image Registration Method Based on Nonlinear Scale Space and Phase Congruency

Multi-Source Remote Sensing Image Registration Method Based ...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Yuejuan Chen Xianglei Li Pingping Huang Weixian Tan Xiujuan Li Bo Yin Mongolia Key Laboratory of Radar Technology and Application College of Information Engineering Inner Mongolia University of Technology Hohhot China Mongolia Key Laboratory of Radar Technology and Application College of Resources and Environmental Engineering Inner Mongolia University of Technology Hohhot China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Due to differences in sensor characteristics, imaging conditions, and time among multi-source remote sensing images, nonlinear changes in image radiance intensity occur, increasing the difficulty of image registration. Existing techniques often face challenges in the robustness and accuracy of feature matching when addressing these nonlinear radiance variations. To solve this problem, this paper proposes a phase congruency remote sensing image registration method based on nonlinear scale space. This method constructs a nonlinear scale space of the image using an improved anisotropic diffusion filter, effectively suppressing noise while preserving the edges and detailed features of the image. It calculates the edge strength and corner strength of the image at different scales using a phase congruency model and extracts feature points from the edge strength map and corner strength map. Multi-scale, multi-direction descriptors are constructed using the response values generated by the Log-Gabor filter to enhance the robustness of feature extraction, thereby achieving precise registration of multi-source remote sensing images. Experimental results show that this method significantly suppresses image noise and nonlinear radiance differences, and outperforms traditional algorithms and other improved registration algorithms regarding the number of correctly matched point pairs and robustness.

关键词： Matched filters image registration Accuracy image edge detection Noise Imaging Feature extraction Robustness Satellite images remote sensing

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Landslide detection using InSAR time series in the Kalekoy dam reservoir (Bingol, Turkiye) 28

Landslide detection using InSAR time series in the Kalekoy d...

引用

conference on image and signal processing for remote sensing XXVIII

作者： Tavus, Beste Kocaman, Sultan Nefeslioglu, Hakan A. Hacettepe Univ Grad Sch Sci & Engn Ankara Turkey Hacettepe Univ Dept Geomat Engn TR-06800 Ankara Turkey Swiss Fed Inst Technol Photogrammetry & Remote Sensing CH-8093 Zurich Switzerland Eskisehir Tech Univ Inst Earth & Space Sci TR-26555 Eskisehir Turkey

ISBN: (纸本)9781510655386;9781510655379

The detection and monitoring of landslides in dam reservoirs are essential for the maintenance and sustainable use of the infrastructure. Sentinel-1 datasets, freely available from European Space Agency (ESA), and Synthetic Aperture Radar Interferometry (InSAR) processing methods provide multitemporal surface change information in terms of deformation and enable engineering geological analyses. The main purpose of this study is to investigate the deformations within the large landslides based on InSAR measurements in the reservoir of the Kalekoy dam, which was built in Bingol Province in the Eastern part of Tiirkiye and filled in 2018. The region is prone to landslides at various magnitudes. For this purpose, the Looking into Continents from Space with Synthetic Aperture Radar (LiCSAR) products were used to apply the Small-Baseline Subsets (SBAS) method for the deformation extraction from Sentinel-1 time series datasets. The activities within the landslides and their surroundings were detected from the deformation maps produced for three periods between December 2015- December 2017 (pre-dam), December 2018-January 2020 (post-dam) and within 2018 (December 2017 - December 2018). Based on the time series analysis results, it was observed that the slope movements increased significantly in 2018, the time of the filling of the reservoir, and in the post-dam periods;and their patterns have also altered.

关键词： Landslide InSAR Kalekoy Dam (Turkiye) deformation analysis Sentinel-1 LiCSAR LiCSBAS

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Side-slither Radiometric Calibration Mode Design and In-orbit Verification of Ziyuan-1(02D) Satellite Hyperspectral imager

Side-slither Radiometric Calibration Mode Design and In-orbi...

引用

2022 International conference on image, signal processing, and Pattern Recognition, ISPP 2022

作者： Liang, Deyin Yu, Jing Han, Bo Zhu, Haijian Institute of Remote Sensing Satellite CAST Beijing100094 China Shanghai Institute of Technical physics Shanghai200083 China

ISBN: (纸本)9781510654846

The radiometric calibration accuracy of a hyperspectral imager is a key link in its quantitative application. The side-slither radiometric calibration can realize the high-frequency and full field of view relative radiometric correction of the hyperspectral imager, which lays the foundation for the subsequent quantitative application of hyperspectral data. In this paper, a hyperspectral imager relative radiometric calibration method and data processing method based on satellite platform 90° yaw maneuver is proposed. A verification of this method is carried out based on the in-orbit data of Ziyuan-1(02D) satellite. The results show that the proposed method has good performance for relative radiometric calibration. The relative radiometric calibration accuracy of all the sensor pixels can reach 1%, and the fringe noise in the original image is well removed. © 2022 SPIE.

关键词： Satellites

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Multimodal Slot Vision Transformer for SAR image Classification

Multimodal Slot Vision Transformer for SAR Image Classificat...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Tianyi Yu Fangzhou Han Lamei Zhang Bin Zou Department of Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In recent years, deep learning network models, particularly Vision Transformer (ViT), have achieved remarkable progress in the domain of computer vision. Radar remote sensing has garnered attention due to its all-weather operational capabilities; However, due to limitations in imaging mechanisms, radar data is susceptible to noise interference, affecting data quality and interpretation accuracy. Hyperspectral data, on the other hand, is known for its extensive spectral coverage. To effectively utilize the modal features of both and promote information exchange between different modalities, addressing the shortcomings of single-modal data and solving the issue of feature indistinguishability in multimodal networks, a ViT network based on slot attention encoder, referred to as SLViT, is proposed. To better achieve feature decoupling, parallel branches are employed to handle remote sensing image blocks from various modalities, while position-based weight sharing is applied, providing a new means to simultaneously leverage spatial information and specific modal information. Meanwhile, unlike traditional multimodal fusion methods, attention mechanisms are employed for feature fusion, and a depthwise separable convolution module is introduced for processing remote sensing image blocks.

关键词： Deep learning Radar remote sensing Computer vision Convolution Semantics Land surface Imaging Radar imaging Transformers Sensors

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