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Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

Mustafa Ustuner, Fusun Balik Sanli
2019 ISPRS International Journal of Geo-Information  
This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine  ...  Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification.  ...  for crop classification using LightGBM, and evaluated the inter-comparison of polarimetric target decomposition in terms of overall and class based accuracies.  ... 
doi:10.3390/ijgi8020097 fatcat:vti2tg3wandwdddz6jhz53n6v4

Crop Classification with Polarimetric Synthetic Aperture Radar Images: Comparative Analysis

Mustafa Ustuner
2021 figshare.com  
data was investigated for crop pattern identification through three different machine learning algorithms (Light Gradient Boosting Machine, Support Vector Machine and Random Forest).  ...  In such a case, polarimetric decomposition methods can be used to extract the three elementary scattering for the targets precisely.  ...  forest (CCF) [68] , extreme gradient boosting (XgBoost) [67] , and Light Gradient Boosting Machine (LightGBM) [70] .  ... 
doi:10.6084/m9.figshare.14376743.v1 fatcat:2h3gcdxgnvgv7gtozrwczdrvz4

Combining Binary and Post-Classification Change Analysis of Augmented ALOS Backscatter for Identifying Subtle Land Cover Changes

Dyah R. Panuju, David J. Paull, Bambang H. Trisasongko
2019 Remote Sensing  
Four datasets were examined for post-classification change analysis including the dual polarimetric backscatter as the benchmark and its augmented data with indices, entropy alpha decomposition and selected  ...  Variable importance was then evaluated to build a best subset model employing seven classifiers, including Bagged Classification and Regression Tree (CAB), Extreme Learning Machine Neural Network (ENN)  ...  We are grateful to Amy Griffin for her support and two anonymous reviewers for their constructive comments leading to a much improved manuscript.  ... 
doi:10.3390/rs11010100 fatcat:7yxe3f7yw5b5plwhi5xcy5wd3e

Integrating Color Features in Polarimetric SAR Image Classification

Stefan Uhlmann, Serkan Kiranyaz
2014 IEEE Transactions on Geoscience and Remote Sensing  
Polarimetric synthetic aperture radar (PolSAR) data are used extensively for terrain classification applying SAR features from various target decompositions and certain textural features.  ...  We then consider support vector machines and random forests classifier topologies to test and evaluate the role of color features over the classification performance.  ...  ACKNOWLEDGMENT The authors would like to thank anonymous reviewers for their help, useful comments, and contribution to this paper's improvement.  ... 
doi:10.1109/tgrs.2013.2258675 fatcat:s4imvqz4ojcjtcx7vfdqhfbgky

Deep Fuzzy Graph Convolutional Networks for PolSAR Imagery Pixel-wise Classification

Hongying Liu, Tianwen Zhu, Fanhua Shang, Yuanyuan Liu, Derui Lv, Yang Shuyuan
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The remote sensing images, especially polarimetric synthetic aperture radar (PolSAR), have provided wide applications for both military and civilian users regardless of weather or lighting conditions.  ...  In this paper, we propose a novel deep fuzzy graph convolutional network (DFGCN) for pixel-wise classification of PolSAR imagery.  ...  The remote sensing images, especially polarimetric synthetic aperture radar (PolSAR), have provided wide applications for both military and civilian users regardless of weather or lighting conditions.  ... 
doi:10.1109/jstars.2020.3041534 fatcat:z3ysjkt275hb5l66epuaatyzym

The Generalized Gamma-DBN for High-Resolution SAR Image Classification

Zhiqiang Zhao, Lei Guo, Meng Jia, Lei Wang
2018 Remote Sensing  
Inspired by deep learning and probability mixture models, a generalized Gamma deep belief network (gΓ-DBN) is proposed for SAR image statistical modeling and land-cover classification in this work.  ...  Performance of the proposed approach is evaluated via several experiments on some high-resolution SAR image patch sets and two large-scale scenes which are captured by ALOS PALSAR-2 and COSMO-SkyMed satellites  ...  crop classification [1] , disaster evaluation [2] , urban extraction [3] and so on.  ... 
doi:10.3390/rs10060878 fatcat:dge5ctv7grhi5nik5wmsvxdsxy

Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau

Jessica da Silva Costa, Veraldo Liesenberg, Marcos Benedito Schimalski, Raquel Valério de Sousa, Leonardo Josoé Biffi, Alessandra Rodrigues Gomes, Sílvio Luís Rafaeli Neto, Edson Mitishita, Polyanna da Conceição Bispo
2021 Remote Sensing  
The polarimetric attributes were evaluated alone and combined with SENTINEL-2A using a supervised classification method based on the Support Vector Machine (SVM) algorithm.  ...  SENTINEL-2A data alone performed better and achieved a weighted overall accuracy and Kappa index of 85.56% and 0.82. This increase was also significant for the Z-test.  ...  Finally, we would like to thank the editors and three reviewers for providing constructive concerns and suggestions. Such feedback helped us improve the quality of the manuscript.  ... 
doi:10.3390/rs13020229 fatcat:ct7ap4whgfdbfkmpi2bjb4k6ti

PSRN:Polarimetric Space Reconstruction Network for PolSAR Image Semantic Segmentation

Hao Jing, Zhirui Wang, Xian Sun, Daifeng Xiao, Kun Fu
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
First, to maintain the relatively initial spatial constraints and the complete polarimetric information, the inputs are arranged by a spatial amplification coding method for the polarimetric coherency  ...  Moreover, it accomplishes the accurate land cover classification for PolSAR images, especially the traditional confusing categories, such as water and roads.  ...  generously providing the raw data used in this paper and all colleagues in the lab for helping to annotate the images.  ... 
doi:10.1109/jstars.2021.3116062 fatcat:ig4xno4iobg27jnbwdmlvb7wmq

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
CDL: A Cloud Detection Algorithm Over Land for MWHS-2 Based on the Gradient Boosting Decision Tree.  ...  ., +, JSTARS 2020 5272-5283 CDL: A Cloud Detection Algorithm Over Land for MWHS-2 Based on the Gradient Boosting Decision Tree.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

2015 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 53

2015 IEEE Transactions on Geoscience and Remote Sensing  
., +, TGRS May 2015 2384-2396 Combination of H-Alpha Decomposition and Migration for Enhancing Subsurface Target Classification of GPR.  ...  ., +, TGRS March 2015 1463-1474 Combination of H-Alpha Decomposition and Migration for Enhancing Subsurface Target Classification of GPR.  ...  Radiofrequency interference A Methodology to Determine Radio-Frequency Interference in AMSR2 Observations.  ... 
doi:10.1109/tgrs.2015.2513444 fatcat:zuklkpk4gjdxjegoym5oagotzq

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging  ...  ., and Drake, V.A., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674  ...  Perez-Suay, A., +, TGRS March 2019 1502-1513 CNN-Based Polarimetric Decomposition Feature Selection for PolSAR Image Classification.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing [article]

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie
2020 arXiv   pre-print
Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification.  ...  This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.  ...  Some key findings such as SVM, RF, and boosted DTs have higher accuracy for classification of remotely sensed data, compared to alternative machine classifiers such as a single DT and K-NN.  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Stamatios Samaras, Eleni Diamantidou, Dimitrios Ataloglou, Nikos Sakellariou, Anastasios Vafeiadis, Vasilis Magoulianitis, Antonios Lalas, Anastasios Dimou, Dimitrios Zarpalas, Konstantinos Votis, Petros Daras, Dimitrios Tzovaras
2019 Sensors  
In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification  ...  However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats.  ...  Each region was then classified by an ensemble consisting of a light 2-layer CNN for feature extraction followed by a Random Forest for classification, as well as a Boosted Random Forest (BRF) operating  ... 
doi:10.3390/s19224837 pmid:31698862 pmcid:PMC6891421 fatcat:rivnqa3uafdpnffieajljuc23a

Support vector machines in remote sensing: A review

Giorgos Mountrakis, Jungho Im, Caesar Ogole
2011 ISPRS journal of photogrammetry and remote sensing (Print)  
A wide range of methods for analysis of airborne-and satellite-derived imagery continues to be proposed and assessed.  ...  In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology.  ...  Acknowledgements Support was provided by the National Science Foundation (award GRS-0648393), by the National Aeronautics and Space Administration (awards NNX08AR11G, NNX09AK16G) and by the Syracuse Center  ... 
doi:10.1016/j.isprsjprs.2010.11.001 fatcat:6hx57jxaxvfxvjoqqmhk5puhty

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

Xiao-Xia Yin, Sillas Hadjiloucas, Yanchun Zhang, Min-Ying Su, Yuan Miao, Derek Abbott
2016 Artificial Intelligence in Medicine  
resonance images Poly(dA-dT)-poly(dT-dA) DNA Tumour microvasculature Basal cell carcinomas a b s t r a c t Objective: We provide a survey of recent advances in biomedical image analysis and classification  ...  Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed.  ...  [175] compared a normalized gradients approach with the mutual information approach for motion correction of DCE-MRI datasets and showed that using cost functions based on normalized gradients can successfully  ... 
doi:10.1016/j.artmed.2016.01.005 pmid:26951630 fatcat:usz7o4ejqbhbxntbu34zhg32iu
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