scholarly journals Seven-Component Model-Based Decomposition for PolSAR Data with Sophisticated Scattering Models

2019 ◽  
Vol 11 (23) ◽  
pp. 2802 ◽  
Author(s):  
Hui Fan ◽  
Sinong Quan ◽  
Dahai Dai ◽  
Xuesong Wang ◽  
Shunping Xiao
Keyword(s):  
Urban Areas ◽  
State Of The Art ◽  
Component Model ◽  
Cross Polarization ◽  
Polarimetric Synthetic Aperture Radar ◽  
Decomposition Scheme ◽  
Model Based ◽  
Component Decomposition ◽  
Oriented Dipole ◽  
Quarter Wave

Due to incomprehensive and inaccurate scattering modeling, the state-of-the-art polarimetric synthetic aperture radar (PolSAR) model-based target decompositions are incapable of effectively depicting the scattering mechanism of obliquely oriented urban areas. In this paper, a seven-component model-based decomposition scheme is proposed by constructing several sophisticated scattering models. First, an eigenvalue-based obliquely-oriented dihedral scattering model is presented to reasonably distribute the co-polarization and cross-polarization scattering powers in obliquely oriented urban areas, thus accurately characterizing the urban scattering. Second, the ±45° oriented dipole and ±45° quarter-wave reflector scattering models are incorporated for the purpose of accounting for the real and imaginary components of the T 13 element in the coherency matrix so as to fully utilize polarimetric information. Finally, according to their mathematical forms, several strategies for model parameter solutions are designed, and the seven-component decomposition is fulfilled. Experimental results conducted on different PolSAR data demonstrate that the proposed method considerably improves the PolSAR scattering interpretation in a more physical manner compared to other existing model-based decomposition, which can be applied for urban area detection, classification, and other urban planning applications.

scholarly journals Integration of Fine Model-Based Decomposition and Guard Filter for Ship Detection in PolSAR Images

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4295
Author(s):  
Dongsheng Liu ◽  
Ling Han
Keyword(s):  
Figure Of Merit ◽  
Spatial Information ◽  
Synthetic Aperture ◽  
Widespread Application ◽  
Polarimetric Synthetic Aperture Radar ◽  
Ship Detection ◽  
Qualitative And Quantitative ◽  
Decomposition Scheme ◽  
Model Based ◽  
Maritime Surveillance

Ship detection with polarimetric synthetic aperture radar (PolSAR) has gained extensive attention due to its widespread application in maritime surveillance. Nevertheless, designing identifiable features to realize accurate ship detection is still challenging. For this purpose, a fine eight-component model-based decomposition scheme is first presented by incorporating four advanced physical scattering models, thus accurately describing the dominant and local structure scattering of ships. Through analyzing the exclusive scattering mechanisms of ships, a discriminative ship detection feature is then constructed from the derived contributions of eight kinds of scattering components. Combined with a spatial information-based guard filter, the efficacy of the feature is further amplified and thus a ship detector is proposed which fulfills the final ship detection. Several qualitative and quantitative experiments are conducted on real PolSAR data and the results demonstrate that the proposed method reaches the highest figure-of-merit (FoM) factor of 0.96, which outperforms the comparative methods in ship detection.

scholarly journals Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Sheng Sun ◽  
Renfeng Liu ◽  
Wen Wen
Keyword(s):  
Unsupervised Classification ◽  
Component Model ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Qualitative And Quantitative ◽  
Four Component Model ◽  
Component Decomposition ◽  
Distance Model ◽  
Radar Imagery ◽  
Synthetic Aperture Radar Imagery

For improving the accuracy of unsupervised classification based on scattering models, the four-component Yamaguchi model is introduced, which is an improved version of the best-known three-component Freeman model. Therewith, the four-component model is combined with the Wishart distance model. The new proposed algorithm of clustering is rolled out thereafter and the procedure of this new method is listed. In experiments, seven areas of various homogeneities are singled out from the Flevoland sample image in AIRSAR dataset. Qualitative and quantitative experiments are performed for a comparative study. It can be easily seen that the resolution and details are remarkably upgraded by the new proposed method. The accuracy of classification in homogeneous areas has also increased significantly by adopting the new iterative algorithm.

scholarly journals Refined Model-Based and Feature-Driven Extraction of Buildings from PolSAR Images

2019 ◽  
Vol 11 (11) ◽  
pp. 1379 ◽  
Author(s):  
Hui Fan ◽  
Sinong Quan ◽  
Dahai Dai ◽  
Xuesong Wang ◽  
Shunping Xiao
Keyword(s):  
Markov Random Fields ◽  
Building Extraction ◽  
Refined Model ◽  
Polarimetric Synthetic Aperture Radar ◽  
Model Based ◽  
Component Decomposition ◽  
The Matrix ◽  
Aerial Vehicle ◽  
The One ◽  
Preliminary Extraction

Polarimetric synthetic aperture radar (PolSAR) building extraction plays an important role in urban planning, disaster management, etc. In this paper, a building extraction method using refined model-based decomposition and robust scattering feature is proposed. On the one hand, the newly proposed refined five-component decomposition and its derived scattering powers are applied to detect the buildings. On the other hand, by combining the matrix elements and co-polarization correlation coefficient, a robust feature is proposed to discriminate buildings and non-buildings. Both these two preliminary extraction results are obtained through thresholding segmentation. Finally, they are fused via the HX Markov random fields so as to further improve the extraction accuracy. The performance of the proposed method is demonstrated and evaluated with Gaofen-3 and uninhabited aerial vehicle SAR full PolSAR data over different test sites. Outputs show that the proposed method outperforms other state-of-the-art methods and provides an overall accuracy of over 90%.

Modelling of activated sludge for textile wastewaters

1998 ◽  
Vol 38 (4-5) ◽  
pp. 9-17 ◽  
Author(s):  
F. Germirli Babuna ◽  
D. Orhon ◽  
E. Ubay Çokgör ◽  
G. Insel ◽  
B. Yaprakli
Keyword(s):  
Activated Sludge ◽  
Comprehensive Evaluation ◽  
Component Model ◽  
Activated Sludge Process ◽  
Experimental Basis ◽  
Model Simulations ◽  
Effluent Quality ◽  
Model Based ◽  
Major Process ◽  
Textile Wastewaters

A comprehensive evaluation of four different textile wastewaters was carried out to set the experimental basis for the modelling of activated sludge process. Experiments involved beside conventional characterization, detailed COD fractionation and assessment of major kinetic and stoichiometric coefficients by means of respirometric measurements. A multi-component model based on the endogenous decay concept was used for the kinetic interpretation and design of activated sludge. The fate and variation of major process components affecting effluent quality with the sludge age were evaluated by means of model simulations.

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

Proximal policy optimization with model-based methods

2022 ◽  
pp. 1-12
Author(s):  
Shuailong Li ◽  
Wei Zhang ◽  
Huiwen Zhang ◽  
Xin Zhang ◽  
Yuquan Leng
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Transition Model ◽  
Practical Applications ◽  
Original Algorithm ◽  
Policy Performance ◽  
Model Based ◽  
Model Free ◽  
Future State ◽  
Policy Optimization

Model-free reinforcement learning methods have successfully been applied to practical applications such as decision-making problems in Atari games. However, these methods have inherent shortcomings, such as a high variance and low sample efficiency. To improve the policy performance and sample efficiency of model-free reinforcement learning, we propose proximal policy optimization with model-based methods (PPOMM), a fusion method of both model-based and model-free reinforcement learning. PPOMM not only considers the information of past experience but also the prediction information of the future state. PPOMM adds the information of the next state to the objective function of the proximal policy optimization (PPO) algorithm through a model-based method. This method uses two components to optimize the policy: the error of PPO and the error of model-based reinforcement learning. We use the latter to optimize a latent transition model and predict the information of the next state. For most games, this method outperforms the state-of-the-art PPO algorithm when we evaluate across 49 Atari games in the Arcade Learning Environment (ALE). The experimental results show that PPOMM performs better or the same as the original algorithm in 33 games.

scholarly journals CHANGE DETECTION AND DEFORMATION ANALYSIS BASED ON MOBILE LASER SCANNING DATA OF URBAN AREAS

2020 ◽  
Vol V-2-2020 ◽  
pp. 703-710
Author(s):  
J. Gehrung ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Laser Scanning ◽  
State Of The Art ◽  
Point Clouds ◽  
Deformation Analysis ◽  
Lidar Data ◽  
Efficient Manner ◽  
Incomplete Observations ◽  
Novel Method

Abstract. Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.

scholarly journals Despeckling PolSAR Images with a Structure Tensor Filter

2020 ◽  
Author(s):  
D Santana-Cedres ◽  
L Gomez ◽  
L Alvarez ◽  
Alejandro Frery
Keyword(s):  
Monte Carlo ◽  
Anisotropic Diffusion ◽  
State Of The Art ◽  
Monte Carlo Analysis ◽  
Structure Tensor ◽  
Synthetic Aperture ◽  
Experimental Setup ◽  
Polarimetric Synthetic Aperture Radar ◽  
Wishart Matrices ◽  
Dominant Direction

© 2004-2012 IEEE. In this letter, we propose a new despeckling filter for fully polarimetric synthetic aperture radar (PolSAR) images defined by 3× 3 complex Wishart distributions. We first generalize the well-known structure tensor to deal with PolSAR data which allows to efficiently measure the dominant direction and contrast of edges. The generalization includes stochastic distances defined in the space of the Wishart matrices. Then, we embed the formulation into an anisotropic diffusion-like schema to build a filter able to reduce speckle and preserve edges. We evaluate its performance through an innovative experimental setup that also includes Monte Carlo analysis. We compare the results with a state-of-the-art polarimetric filter.

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