| 1. |
|
|
| 2. |
- Alves, Dimas I, et al.
(författare)
-
A Statistical Analysis for Wavelength-Resolution SAR Image Stacks
- 2020
-
Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1545-598X .- 1558-0571. ; 17:2, s. 227-231
-
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.
|
|
| 3. |
- Alves, Dimas I., et al.
(författare)
-
Cooperative multiple-access scheme with antenna selection and incremental relaying
- 2014
-
Ingår i: 2014 INTERNATIONAL TELECOMMUNICATIONS SYMPOSIUM (ITS). - São Paulo : IEEE. - 9781479937431
-
Konferensbidrag (refereegranskat)abstract
- A cooperative multiple-access scheme for wireless communications systems with antenna selection and incremental relaying is proposed. The scheme aims to improve the system throughput while preserving good performance in terms of bit error rate. The system consists of N nodes which send their information to both the destination node and the multiple-antenna relay station. Based on the channel state information, the destination node decides whether or not relaying will be performed. When the relaying is performed, the decode-remodulate-and-forward protocol is used with the best antenna. Results reveal that the proposed scheme achieves a good tradeoff between throughput and bit error rate, which makes suitable to be considered for multi-user networks.
|
|
| 4. |
- Alves, Dimas, I, et al.
(författare)
-
Incoherent Change Detection Methods for Wavelength-Resolution SAR Image Stacks Based on Masking Techniques
- 2020
-
Ingår i: 2020 IEEE National Radar Conference - Proceedings. - : IEEE. - 9781728189420
-
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.
|
|
| 5. |
- Alves, Dimas I., et al.
(författare)
-
Neyman-Pearson Criterion-Based Change Detection Methods for Wavelength-Resolution SAR Image Stacks
- 2022
-
Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1545-598X .- 1558-0571. ; 19
-
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
|
|
| 6. |
- Alves, Dimas I, et al.
(författare)
-
Statistical Analysis for Wavelength-Resolution SARImage Stacks : New Case Studies
- 2020
-
Ingår i: XXXVIII SIMPÓSIO BRASILEIRO DE TELECOMUNICAÇÕES E PROCESSAMENTO DE SINAIS.
-
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.
|
|
| 7. |
- da Silva, Fabiano G., et al.
(författare)
-
Hybrid Feature Extraction Based on PCA and CNN for Oil Rig Classification in C-Band SAR Imagery
- 2022
-
Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE - International Society for Optical Engineering.
-
Konferensbidrag (refereegranskat)abstract
- Feature extraction techniques play an essential role in classifying and recognizing targets in synthetic aperture radar (SAR) images. This article proposes a hybrid feature extraction technique based on convolutional neural networks and principal component analysis. The proposed method is used to extract features of oil rigs and ships in C-band synthetic aperture radar polarimetric images obtained with the Sentinel-1 satellite system. The extracted features are used as input in the logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision tree (DT), and k-nearest-neighbors (kNN) classification algorithms. Furthermore, the statistical tests of Kruskal-Wallis and Dunn were considered to show that the proposed extraction algorithm has a significant impact on the performance of the classifiers. © 2022 SPIE.
|
|
| 8. |
- Moreira, André R., et al.
(författare)
-
Classification of Oil Rigs in SAR Images Using RPCA-Based Preprocessing
- 2024
-
Ingår i: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR. - : Institute of Electrical and Electronics Engineers (IEEE). - 9783800762873 ; , s. 432-437
-
Konferensbidrag (refereegranskat)abstract
- This paper uses a signal separation method called Robust Principal Component Analysis (RPCA) as a pre-processing technique to improve the classification of oil rigs in Synthetic Aperture Radar (SAR) images. After the pre-processing method, features are extracted from the images using the VGG-16 convolutional neural network. These features guide classification through Support Vector Machine (SVM), Neural Networks, and Logistic Regression algorithms. The experiments used SAR images from the Sentinel-1 system, C-band, and VH polarization. Early results highlight that preprocessing improves classification accuracy compared to conventional methods. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.
|
|
| 9. |
- Ramos, Lucas P., et al.
(författare)
-
A wavelength-resolution sar change detection method based on image stack through robust principal component analysis
- 2021
-
Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:5, s. 1-16
-
Tidskriftsartikel (refereegranskat)abstract
- Recently, it was demonstrated that low-frequency wavelength-resolution synthetic aperture radar (SAR) images could be considered to follow an additive mixing model due to their backscatter characteristics. This simplification allows for the use of source separation methods, such as robust principal component analysis (RPCA) via principal component pursuit (PCP), for detecting changes in those images. In this manuscript, a change detection method for wavelength-resolution SAR images based on image stack through RPCA is proposed. The method aims to explore both the temporal and flight heading diversity of a set of wavelength-resolution multitemporal SAR images in order to detect concealed targets in forestry areas. A heuristic based on three rules for better exploring the RPCA results is introduced, and a new configurable parameter for false alarm reduction based on the analysis of image windows is proposed. The method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high-frequency (VHF) SAR system CARABAS-II. Experiments for stacks of four and seven reference images are conducted, and the use of reference images acquired with different flight headings is explored. The results indicate that a gain in performance can be achieved by using large image stacks containing, at least, one image of each possible flight heading of the data set, which can result in a probability of detection (PD) above 99% for a false alarm rate (FAR) as low as one false alarm per three square kilometers. Furthermore, it is demonstrated that high PD and low FAR can be achieved, also considering images from similar flight headings as reference images. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
|
|
| 10. |
- Ramos, Lucas P., et al.
(författare)
-
Robust Principal Component Analysis Techniques for Ground Scene Estimation in SAR Imagery
- 2023
-
Ingår i: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1939-1404 .- 2151-1535. ; 16, s. 9697-9710
-
Tidskriftsartikel (refereegranskat)abstract
- Robust principal component analysis (RPCA) has been widely used for processing and interpreting high-dimensional data in different applications such as data classification, face recognition, video analytics, and recommendation system design. However, the advancement of multisensor-based technologies and the emergence of large data sets have highlighted the limitations of traditional matrix-based models, which have paved the way for higher-order extensions such as tensor RPCA (TRPCA) techniques. These signal separation techniques can be useful for ground scene estimation (GSE) in synthetic aperture radar (SAR) imagery. GSE estimates the clutter-plus-noise content in the scene, and therefore, change detection (CD) methods can benefit, reducing the number of false alarms (FA). This paper presents two new GSE methods for SAR imagery based on robust PCA techniques. The first proposed method uses the RPCA via Principal Component Pursuit (PCP) to obtain the GSE-RPCA. The second method uses TRPCA via New Tensor Nuclear Norm (TNN) to obtain the GSE-TRPCA. The methodology allows the GSE to be obtained through a generalized regularization parameter. The alternating direction method of multipliers (ADMM) algorithm is utilized to solve both optimization problems. Experimental results are evaluated considering real SAR imagery from two data sets acquired with the CARABAS II and ALOS PALSAR systems, respectively. Additionally, the proposed techniques were evaluated under several input characteristics, e.g., eight-image stacks and image pairs. Both GSE techniques are more robust to outliers and missing data when compared to existing solutions found in the literature. Finally, GSE-TRPCA achieved a minimum-square error performance of 0.0018 for some of the evaluated scenarios.
|
|
