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| 2. |
- Molin, Ricardo D., Jr., et al.
(författare)
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A CHANGE DETECTION ALGORITHM FOR SAR IMAGES BASED ON LOGISTIC REGRESSION
- 2019
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Ingår i: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019). - : IEEE. - 9781538691540 ; , s. 1514-1517
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Konferensbidrag (refereegranskat)abstract
- This paper presents an incoherent change detection algorithm (CDA) for synthetic aperture radar (SAR) images based on logistic regression. The input data consists of a set of 24 SAR images acquired in a test site in northern Sweden [1]. Subsets of these images are trained based on pixel amplitude, flight heading and neighboring features such as local mean, standard deviation and skewness. The proposed method intends to explore the advantadges from both pixel- and object-based approaches, while evaluating multiple features in amplitude only SAR images. Preliminary results based on K-fold cross validation have shown that the proposed CDA achieves good performance when compared to the results presented in [1].
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| 3. |
- Palm, Bruna, et al.
(författare)
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2-D Rayleigh autoregressive moving average model for SAR image modeling
- 2022
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier B.V.. - 0167-9473 .- 1872-7352. ; 171
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Tidskriftsartikel (refereegranskat)abstract
- Two-dimensional (2-D) autoregressive moving average (ARMA) models are commonly applied to describe real-world image data, usually assuming Gaussian or symmetric noise. However, real-world data often present non-Gaussian signals, with asymmetrical distributions and strictly positive values. In particular, SAR images are known to be well characterized by the Rayleigh distribution. In this context, the ARMA model tailored for 2-D Rayleigh-distributed data is introduced—the 2-D RARMA model. The 2-D RARMA model is derived and conditional likelihood inferences are discussed. The proposed model was submitted to extensive Monte Carlo simulations to evaluate the performance of the conditional maximum likelihood estimators. Moreover, in the context of SAR image processing, two comprehensive numerical experiments were performed comparing anomaly detection and image modeling results of the proposed model with traditional 2-D ARMA models and competing methods in the literature. © 2022 The Authors
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| 4. |
- Palm, Bruna G, et al.
(författare)
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Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack
- 2020
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 20:7
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0 . 11 / km 2 , when considering military vehicles concealed in a forest.
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| 5. |
- Palm, Bruna, et al.
(författare)
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Inflated Rayleigh Distribution for SAR Imagery Modeling
- 2022
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Ingår i: International Geoscience and Remote Sensing Symposium (IGARSS 2022). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665427920 ; , s. 44-47
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Konferensbidrag (refereegranskat)abstract
- Synthetic aperture radars (SAR) data plays an important role in remote sensing applications. It is common knowledge that SAR image amplitude pixels can be approximately modeled by the Rayleigh distribution. However, this model is contin-uous and does not accommodate points with non-zero prob-ability, such as a null pixel amplitude value. Thus, in this paper, we propose an inflated Rayleigh distribution for SAR image modeling that is based on a mixed continuous-discrete distribution and can be used to fit signals with observed values on [0, infty). The maximum likelihood approach is considered to estimate the parameters of the proposed distribution. An empirical experiment with a SAR image is also presented and discussed. © 2022 IEEE.
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| 6. |
- Palm, Bruna, et al.
(författare)
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Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers
- 2022
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 0196-2892 .- 1558-0644. ; 60
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Tidskriftsartikel (refereegranskat)abstract
- The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This article aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the nonrobust estimators show a relative bias value 65-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about 96% and 10%, respectively, in the mean absolute value of both measures, in compassion to the nonrobust estimators. Moreover, two SAR datasets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature. © 2022 IEEE.
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