Article
Geochemistry & Geophysics
Muhammad Amjad Iqbal, Andrei Anghel, Mihai Datcu
Summary: This letter proposes a novel method for coastline extraction using synthetic aperture radar data, which relies on the Doppler parameter to delineate coastlines in the absence of in-situ data and cloud-free optical images. Results indicate that utilizing scattering from dual and cross-polarization for coastline extraction is more reliable than using co-polarization.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Environmental Sciences
Xueyun Wei, Wei Zheng, Caiping Xi, Shang Shang
Summary: This paper proposes a method for detecting the land-sea boundary based on a geometric active contour model, improving the traditional method for extracting shorelines from SAR images and increasing stability and accuracy.
Article
Automation & Control Systems
Wei Wei, Yongjie Shu, Jianfeng Liu, Linwei Dong, Leilei Jia, Jianfeng Wang, Yan Guo
Summary: In this paper, an innovative contour extraction method based on hierarchical features is proposed, which can effectively extract object contours from aerial images with complex backgrounds, improving the efficiency and accuracy of power inspections and promoting the automation level in power engineering.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Mathematical & Computational Biology
Xiaojun Liang
Summary: This paper proposes a video images-aware knowledge extraction model for intelligent healthcare management of basketball players, which can accurately capture and characterize the shooting routes of basketball players, providing effective tools and methods for player health management.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Shudi Yang, Jiaxiong Wu, Zhipeng Feng
Summary: The research introduces a dual-fusion active contour model with semantic information to improve the accuracy of contour extraction in underwater images. Experimental results show that the proposed model achieves the best results in MAE, ER, and DR indicators, providing reliable prior knowledge for target tracking and visual information mining.
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Sciences
Junnan Huang, Daoxiang An, Yuxiao Luo, Jingwei Chen, Zhimin Zhou, Leping Chen, Dong Feng
Summary: As SAR technology advances, SAR-image data is becoming more abundant, and the fusion of dual-frequency SAR images can describe targets more comprehensively by combining advantages of different frequencies; however, accurate registration between dual-frequency SAR images is challenging due to complex geometric distortion and gray variance.
Article
Green & Sustainable Science & Technology
Hongxia Zheng, Xiao Li, Jianhua Wan, Mingming Xu, Shanwei Liu, Muhammad Yasir
Summary: Coastlines of different types are important for tourism, coastal zone management, and marine engineering. Extracting coastline accurately and quickly from satellite images without manual intervention is a challenging task. This paper proposes an improved method for instant waterline extraction using various image processing techniques.
Article
Computer Science, Artificial Intelligence
Puneet Kumar, R. K. Agrawal, Dhirendra Kumar
Summary: In this study, an improved fuzzy bounded k-plane clustering method (FBkPC_S1) is proposed, which efficiently clusters non-spherically distributed data and handles noise. The method utilizes FCM objective function to constrain cluster planes and incorporates local spatial information in the objective function of FkPC to handle noise. Extensive experiments on synthetic image and human brain MRI datasets demonstrate the fast and robust performance of the proposed method in providing accurate segmentation in the presence of noise artifacts. The proposed FBkPC_S1 method achieves superior average segmentation accuracy and Dice score compared to related methods.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Dongsheng Liu, Ling Han
Summary: In this study, a coastline detection method using a Gaussian Mixture Model (GMM) and a K distribution-based local statistical active contour model (LKDACM) for synthetic aperture radar (SAR) imagery was proposed. The method overcame the inaccuracies caused by SAR data noise and the sensitivity to initial position of traditional active contour models, and achieved accurate and fast coastline extraction for detecting coastline changes.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2022)
Article
Environmental Sciences
Gongtang Wang, Fuyu Bo, Xue Chen, Wenfeng Lu, Shaohai Hu, Jing Fang
Summary: Speckle is a common noise-like phenomenon in SAR imaging, and numerous despeckling methods have been proposed. However, existing methods have limitations in preserving structures and suppressing speckle effectively. In this study, a combination of two state-of-the-art despeckling tools is designed, and clustering and GLCM are used for image classification and weighting. Experimental results demonstrate the potential of the proposed method in removing speckle and preserving structural details.
Article
Engineering, Aerospace
T. Yu, S. W. Xu, B. Y. Tao, W. Z. Shao
Summary: Continuous temporal and spatial monitoring of the coastline is crucial for environmental protection. This study used multi-source satellite remote sensing images to extract waterlines and proposed a threshold segmentation method. Accuracy assessment revealed differences among datasets, but the fusion of optical and microwave remote sensing images allows for better coastline monitoring.
ADVANCES IN SPACE RESEARCH
(2022)
Review
Computer Science, Interdisciplinary Applications
Marcin Ciecholewski
Summary: Synthetic aperture radar (SAR) images contain necessary information for investigating coastlines. This article presents an overview of state-of-the-art segmentation methods used for detecting and extracting coasts from SAR images, and discusses their advantages, disadvantages, and evaluation metrics used. The segmentation methods can be classified into three main groups: thresholding methods, active contours, and machine learning approaches.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Information Systems
Xin Dong, Yan Liang, Jie Wang
Summary: A distributed clustering method based on spatial information is proposed in this paper, which marks the associated pulse based on spatial information within the target area of interest and then re-clusters the pulse using marked information and pulse parameters to achieve effective target separation.
Article
Environmental Sciences
Xinran Ji, Liang Huang, Bo-Hui Tang, Guokun Chen, Feifei Cheng
Summary: This paper proposes a superpixel spatial intuitionistic fuzzy C-means (SSIFCM) clustering algorithm for pixel-level unsupervised classification of high spatial resolution remote sensing (HSRRS) images. The algorithm utilizes superpixel segmentation to obtain local spatial information, constructs a superpixel spatial intuitionistic fuzzy membership matrix, and uses spectral features and local relations between adjacent superpixels for classification. The results show that the proposed SSIFCM algorithm achieves the highest accuracy among fifteen existing unsupervised classification algorithms, demonstrating its effectiveness in improving the classification accuracy of HSRRS images.
Article
Geochemistry & Geophysics
Wenbo Yu, Miao Zhang, Yi Shen
Summary: The article proposes a novel unsupervised hyperspectral feature extraction architecture based on spatial revising variational autoencoder, which extracts spatial features from multiple aspects and revises the acquired spectral features. Experimental results demonstrate that this method outperforms comparison methods and is expected to play a significant role in hyperspectral image processing.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Engineering, Aerospace
Mohammad Modava, Gholamreza Akbarizadeh, Mohammad Soroosh
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2019)
Article
Environmental Sciences
Foroogh Sharifzadeh, Gholamreza Akbarizadeh, Yousef Seifi Kavian
JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
(2019)
Article
Computer Science, Artificial Intelligence
Neda Ahmadi, Gholamreza Akbarizadeh
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Engineering, Electrical & Electronic
Amal Eisapour Moghaddam, Gholamreza Akbarizadeh, Hooman Kaabi
SIGNAL IMAGE AND VIDEO PROCESSING
(2019)
Article
Computer Science, Artificial Intelligence
Farnaam Samadi, Gholamreza Akbarizadeh, Hooman Kaabi
IET IMAGE PROCESSING
(2019)
Article
Computer Science, Theory & Methods
Fatemeh Taibi, Gholamreza Akbarizadeh, Ebrahim Farshidi
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
(2019)
Article
Remote Sensing
Moein Zalpour, Gholamreza Akbarizadeh, Navid Alaei-Sheini
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2020)
Article
Engineering, Multidisciplinary
Zeinab Tirandaz, Gholamreza Akbarizadeh, Hooman Kaabi
Article
Optics
Asghar Askarian, Gholamreza Akbarizadeh, Mehdi Fartash
Article
Engineering, Electrical & Electronic
Noushin Davari, Gholamreza Akbarizadeh, Elaheh Mashhour
Summary: This research introduces a deep learning-based UV-Visible video processing method to identify issues in power distribution lines by detecting incipient fault types and severity levels. The method extracts video frames, detects power devices, identifies corona discharges using color thresholding, and ultimately determines the severity of incipient faults.
IEEE TRANSACTIONS ON POWER DELIVERY
(2021)
Article
Remote Sensing
Nastaran Aghaei, Gholamreza Akbarizadeh, Abdolnabi Kosarian
Summary: This paper presents a new oil spill detection algorithm based on the level set method in marine areas. The algorithm combines multi-objective grey wolf optimization and K-means clustering to find the optimal threshold level for dark spot detection in SAR images, and utilizes feature extraction and classification to identify oil-suspicious areas. Experimental results demonstrate the reliability and robustness of the proposed method, even for noisy images with heterogeneous and weak boundaries.
EUROPEAN JOURNAL OF REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Noushin Davari, Gholamreza Akbarizadeh, Elaheh Mashhour
Summary: This paper presents a deep learning-based method for defect detection and classification of power distribution lines using video analysis. The proposed method demonstrates better performance than the existing techniques and is practical with minimal dependence on environmental conditions.
IEEE TRANSACTIONS ON POWER DELIVERY
(2022)
Article
Environmental Sciences
Nastaran Aghaei, Gholamreza Akbarizadeh, Abdolnabi Kosarian
Summary: In this article, ShuffleNet blocks are used to detect oil spills in SAR images, which is more effective than other methods. The proposed method improved the mIoU by 7.1% over the best results of some previous methods.
GEOCARTO INTERNATIONAL
(2022)
Article
Engineering, Electrical & Electronic
Fatemeh Mahmoudi, Shahriar Baradaran Shokouhi, Gholamreza Akbarizadeh
Summary: This article explores the use of SAR imaging and deep neural networks for oil spill detection, finding that the U-NET network is the most accurate in identifying oil spills in SAR images. The authors increased the number of input images and trained two convolutional neural networks to achieve their results.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Engineering, Biomedical
Nahid Chegeni, Mohamad Javad Tahmasebi Birgani, Fariba Farhadi Birgani, Daryoush Fatehi, Gholamreza Akbarizadeh, Marziyeh Tahmasbi
JOURNAL OF MEDICAL SIGNALS & SENSORS
(2019)