| 1. |
- Alves, Dimas I, et al.
(författare)
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A Statistical Analysis for Wavelength-Resolution SAR Image Stacks
- 2020
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1545-598X .- 1558-0571. ; 17:2, s. 227-231
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Tidskriftsartikel (refereegranskat)abstract
- This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59 & x0025; of the tested samples passed the Anderson-Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.
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| 2. |
- Alves, Dimas irion, et al.
(författare)
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Change Detection Method for Wavelength-Resolution SAR Images Based on Bayes’ Theorem : An Iterative Approach
- 2023
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Ingår i: IEEE Access. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 84734-84743
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods. Author
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| 3. |
- Alves, Dimas, I, et al.
(författare)
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Incoherent Change Detection Methods for Wavelength-Resolution SAR Image Stacks Based on Masking Techniques
- 2020
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Ingår i: 2020 IEEE National Radar Conference - Proceedings. - : IEEE. - 9781728189420
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Konferensbidrag (refereegranskat)abstract
- This paper presents two incoherent change detection methods for wavelength-resolution synthetic aperture radars (SAR) image stacks based on masking techniques. The first technique proposed is the Simple Masking Detection (SMD). This method uses the statistical behavior of pixels-sets in the image stack to create a binary mask, which is used to remove pixels that are not related to changes in a surveillance image from the same interest region. The second technique is the Multiple Concatenated Masking Detection (MCMD), which produces a more selective mask than the SMD by concatenating multiple masks from different image stacks. The MCMD can be used in specific applications where multiple stacks share common patterns of target deployments. Both proposed techniques were evaluated using 24 incoherent SAR images obtained by the CARABAS II system. The experimental results revealed that the proposed detection methods have better performance in terms of probability of detection and false alarm rate when compared with other change detection techniques, especially for high detection probabilities scenarios.
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| 4. |
- Alves, Dimas I., et al.
(författare)
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Neyman-Pearson Criterion-Based Change Detection Methods for Wavelength-Resolution SAR Image Stacks
- 2022
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1545-598X .- 1558-0571. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman-Pearson criterion. The first proposed method uses the data from wavelength-resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considers a priori information about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods. CCBY
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| 5. |
- Alves, Dimas I, et al.
(författare)
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Statistical Analysis for Wavelength-Resolution SARImage Stacks : New Case Studies
- 2020
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Ingår i: XXXVIII SIMPÓSIO BRASILEIRO DE TELECOMUNICAÇÕES E PROCESSAMENTO DE SINAIS.
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Konferensbidrag (refereegranskat)abstract
- This paper presents new case studies for thestatistical analysis for wavelength resolution SAR image stacks.The statistical analysis considers the Anderson-Darling goodnessof-fit test in a set of pixel samples from the same position obtainedfrom a SAR image stack. The test is applied in wavelengthresolution SAR image stacks. The present work consists of twocase studies based on the use of multiple-pass stacks and TypeI error using the False Discovery Rate controlling procedures.In addition, an application scenario is presented for the studiedscenarios.
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| 6. |
- Alves, Dimas Irion, et al.
(författare)
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Wavelength-Resolution SAR Change Detection Using Bayes' Theorem
- 2020
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Ingår i: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1939-1404 .- 2151-1535. ; 13, s. 5560-5568
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Tidskriftsartikel (refereegranskat)abstract
- This article presents Bayes' theorem for wavelength-resolution synthetic aperture radar (SAR) change detection method development. Different change detection methods can be derived using Bayes' theorem in combination with the target model, clutter-plus-noise model, iterative implementation, and noniterative implementation. As an example of the Bayes' theorem use for wavelength-resolution SAR change detection method development, we propose a simple change detection method with a clutter-plus-noise model and noniterative implementation. In spite of simplicity, the proposed method provides a very competitive performance in terms of probability of detection and false alarm rate. The best result was a probability of detection of $\text{98.7}\%$ versus a false alarm rate of one per square kilometer.
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| 7. |
- Araujo, Gustavo F., et al.
(författare)
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A Tailored cGAN SAR Synthetic Data Augmentation Method for ATR Application
- 2023
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Ingår i: Proceedings of the IEEE Radar Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665436694
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Konferensbidrag (refereegranskat)abstract
- This article proposes a method to simulate Synthetic Aperture Radar (SAR) targets for specific incidence and azimuth angles. Images synthesized by Electromagnetic Computing (EMC) are used to train a Conditional Generative Adversarial Network (cGAN). Two synthetic image chips of the same class and incidence angle, separated by two degrees in azimuth, are used as input to the cGAN. The cGAN predicts the image of the same class and incidence angle whose azimuth angle corresponds to the bisector of the two input chips. An evaluation using the SAMPLE dataset was performed to verify the quality of the image prediction. Running through a total of 100 training epochs, the cGAN converges, reaching the best Mean Squared Error (MSE) after 77 epochs. The results demonstrate that the proposed method is promising for Automatic Target Recognition (ATR) applications. © 2023 IEEE.
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| 8. |
- Araujo, Gustavo F., et al.
(författare)
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Assessment of preprocessing techniques in a model-based automatic target recognition algorithm for the SAMPLE dataset
- 2022
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Ingår i: Image and Signal Processing for Remote Sensing XXVIII 2022. - : SPIE - International Society for Optical Engineering. - 9781510655379
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Konferensbidrag (refereegranskat)abstract
- This article investigates basic preprocessing techniques to improve classification accuracy in the context of Automatic Target Recognition (ATR) of non-cooperative targets in Synthetic Aperture Radar (SAR) images. Preprocessing techniques are considered in synthetic data providing different inputs to a model-based classification algorithm. Experiments with preprocessing techniques such as area reduction, morphological transformations, and speckle filtering were run using ten target classes of the SAMPLE dataset. The classification is performed in measure data using scattering centers as features. The results reveal that the original image without any preprocessing techniques reached the best classification performance. However, investigations with other classifiers that use different features may benefit from such preprocessing techniques. © 2022 SPIE.
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| 9. |
- Araujo, Gustavo F., et al.
(författare)
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Non-Cooperative SAR Automatic Target Recognition Based on Scattering Centers Models
- 2022
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:3
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Tidskriftsartikel (refereegranskat)abstract
- This article proposes an Automatic Target Recognition (ATR) algorithm to classify non-cooperative targets in Synthetic Aperture Radar (SAR) images. The scarcity or nonexistence of measured SAR data demands that classification algorithms rely only on synthetic data for training purposes. Based on a model represented by the set of scattering centers extracted from purely synthetic data, the proposed algorithm generates hypotheses for the set of scattering centers extracted from the target under test belonging to each class. A Goodness of Fit test is considered to verify each hypothesis, where the Likelihood Ratio Test is modified by a scattering center-weighting function common to both the model and target. Some algorithm variations are assessed for scattering center extraction and hypothesis generation and verification. The proposed solution is the first model-based classification algorithm to address the recently released Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset on a 100% synthetic training data basis. As a result, an accuracy of 91.30% in a 10-target test within a class experiment under Standard Operating Conditions (SOCs) was obtained. The algorithm was also pioneered in testing the SAMPLE dataset in Extend Operating Conditions (EOCs), assuming noise contamination and different target configurations. The proposed algorithm was shown to be robust for SNRs greater than −5 dB. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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| 10. |
- Araujo, Gustavo F., et al.
(författare)
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Synthetic SAR Data Generator Using Pix2pix cGAN Architecture for Automatic Target Recognition
- 2023
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 143369-143386
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Tidskriftsartikel (refereegranskat)abstract
- Synthetic Aperture Radar (SAR) technology has unique advantages but faces challenges in obtaining enough data for noncooperative target classes. We propose a method to generate synthetic SAR data using a modified pix2pix Conditional Generative Adversarial Networks (cGAN) architecture. The cGAN is trained to create synthetic SAR images with specific azimuth and elevation angles, demonstrating its capability to closely mimic authentic SAR imagery through convergence and collapsing analyses. The study uses a model-based algorithm to assess the practicality of the generated synthetic data for Automatic Target Recognition (ATR). The results reveal that the classification accuracy achieved with synthetic data is comparable to that attained with original data, highlighting the effectiveness of the proposed method in mitigating the limitations imposed by noncooperative SAR data scarcity for ATR. This innovative approach offers a promising solution to craft customized synthetic SAR data, ultimately enhancing ATR performance in remote sensing.
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