| 1. |
- Campos, Alexandre B., et al.
(author)
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Adaptive Target Enhancer : Bridging the Gap between Synthetic and Measured SAR Images for Automatic Target Recognition
- 2023
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In: Proceedings of the IEEE Radar Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665436694
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Conference paper (peer-reviewed)abstract
- Automatic target recognition (ATR) algorithms have been successfully used for vehicle classification in synthetic aperture radar (SAR) images over the past few decades. For this application, however, the scarcity of labeled data is often a limiting factor for supervised approaches. While the advent of computer-simulated images may result in additional data for ATR, there is still a substantial gap between synthetic and measured images. In this paper, we propose the so-called adaptive target enhancer (ATE), a tool designed to automatically delimit and weight the region of an image that contains or is affected by the presence of a target. Results for the publicly released Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset show that, by defining regions of interest and suppressing the background, we can increase the classification accuracy from 68% to 84% while only using artificially generated images for training. © 2023 IEEE.
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| 2. |
- Campos, Alexandre Becker, 1997-, et al.
(author)
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UNSUPERVISED AUTOMATIC TARGET DETECTION FOR MULTITEMPORAL SAR IMAGES BASED ON ADAPTIVE K-MEANS ALGORITHM
- 2020
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Conference paper (peer-reviewed)abstract
- In this paper, we present an unsupervised automatic target detection algorithm for multitemporal SAR images. The proposed two-fold method is expected to reduce processing time for large scene sizes with sparse targets while still improving detection performance. Firstly, pixel blocks are extracted from an initial change map to reduce the algorithm's search space and favor target detection. Secondly, an adaptive k-means algorithm selects the number of clusters that better separates targets from false alarms, which are discarded. Preliminary results show the advantages of the proposed method in processing time and detection performance over a recently proposed supervised method for the CARABAS-II dataset.
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| 3. |
- Fabrin, Ana, et al.
(author)
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A CFAR optimization for low frequency UWB SAR change detection algorithms
- 2017
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In: International Geoscience and Remote Sensing Symposium (IGARSS). - : Institute of Electrical and Electronics Engineers Inc.. - 9781509049516 ; , s. 1071-1074
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Conference paper (peer-reviewed)abstract
- This paper presents a study on the constant false alarm rate (CFAR) filter design for change detection algorithms (CDA). More specifically, we are interested in CFAR filters used in CDA for low frequency ultra-wideband (UWB) synthetic aperture radar (SAR) systems. The filter design performance was evaluated in terms of false alarm rate (FAR) and probability of detection (PD). For evaluation purposes, we considered a set of SAR images obtained with the CARABAS-II system. The results are compared with the ones presented in [1], where the same CDA was considered, except for the CFAR filter. The results show that relevant FAR performance improvements can be obtained by just modifying the CFAR filter parameters taking into account the image resolution and target characteristics. © 2017 IEEE.
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| 4. |
- Molin, Ricardo D., Jr., et al.
(author)
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A CHANGE DETECTION ALGORITHM FOR SAR IMAGES BASED ON LOGISTIC REGRESSION
- 2019
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In: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019). - : IEEE. - 9781538691540 ; , s. 1514-1517
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Conference paper (peer-reviewed)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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