Wavelength-Resolution SAR Change Detection Using Bayes’ Theorem

Alves, Dimas I.; Palm, Bruna Gregory; Hellsten, Hans; Vu, Viet T.; Pettersson, Mats I.; Machado, Renato; Uchôa-Filho, Bartolomeu F.; Dammert, Patrik

doi:10.1109/jstars.2020.3025089

Cited by 13 publications

(32 citation statements)

References 25 publications

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“…As can be noted, experiment 18 has the lowest performance when comparing with the others. This behavior was observed and discussed before [21]. To obtain better performance for this specific experiment, additional processing is possibly required [3].…”

Section: Resultssupporting

confidence: 54%

Target Detection Method for Intensity VHF Wavelength-Resolution SAR Images

Voigt¹,

Alves²,

Müller³

et al. 2021

Anais Do XXXIX Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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This paper presents a detection method for intensity very-high frequency (VHF) wavelength-resolution synthetic aperture radar (SAR) images. The method is based on the likelihoodratio test, considering a bivariate exponential distribution for the tested hypothesis. A common image is used for obtaining two difference images, one considering a surveillance image and another, a reference image. The method uses the difference images to reduce the number of false alarms related to strong scatterers not related to targets. The method is assessed through received operational curves with VHF ultrawideband SAR images obtained by the CARABAS II system. The experimental results reveal the potential of the proposed approach compared with other detection methods.

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Section: Resultssupporting

confidence: 54%

Target Detection Method for Intensity VHF Wavelength-Resolution SAR Images

Voigt¹,

Alves²,

Müller³

et al. 2021

Anais Do XXXIX Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…For the application considered in this paper, similar morphological operations like the ones used in Alves and Vu. 14,18 Additionally, the same structuring elements were applied. These operations are one erosion followed by dilatations, which mitigate the detection of changes related to isolated pixels and the multiple detections of the same targets.…”

Section: Change Detection Methodsmentioning

confidence: 99%

Change detection method for intensity VHF wavelength-resolution SAR images

Voigt

Alves

Müller

et al. 2021

Image and Signal Processing for Remote Sensing XXVII

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This paper presents the proposal of a new change detection method for intensity VHF wavelength-resolution images. High-amplitude pixels are related to the presence of strong scatterers, resulting in high detection probability performance. However, the number of false alarms tends to be high too. To reduce the influence of the strong scatterers that are not related to targets, i.e., present in both surveillance and reference images, in this initial study, difference images are considered. The proposed change detection method is based on a likelihood-ratio test, where the tested hypothesis is the bivariate exponential distribution. The derivation of the proposed likelihood test is presented. Finally, the proposed change detection method is assessed considering data measured with the CARABAS II VHF UWB SAR system. Preliminary results show that the proposed method is efficient in detecting both positive and negative changes.

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“…The changes related to this data set are related to the target's location and orientation for each deployment. Efficient change detection methods of targets concealed by forests are widely explored in the literature; see, e.g., [21]- [23], [30], [31], [33]- [35]. All these studies consider as input a difference image, obtained by a simple subtraction between two images (reference and monitored image).…”

Section: Introductionmentioning

confidence: 99%

Change Detection Based on Convolutional Neural Networks Using Stacks of Wavelength-Resolution Synthetic Aperture Radar Images

Vinholi

Palm

Silva

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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This article presents two supervised change detection algorithms (CDA) based on convolutional neural networks (CNN) that use stacks of co-registered wavelength-resolution synthetic aperture radar (SAR) images to detect changes in an image under monitoring. The additional information of a scene of interest provided by SAR image stacks can be explored to enhance the performance of change detection algorithms. In particular, stacks of images with similar statistics can be obtained for ultra-wideband (UWB) very high frequency (VHF) SAR systems, as they produce images highly stable in time. The proposed CDAs can be summed up into four stages: difference image formation, semantic segmentation, clustering, and change classification. The CNN-GSP algorithm is based on a ground scene prediction (GSP) image, which is used as a reference to form a difference image (DI). A CNN-based model then analyzes the DI. The CNN-MDI algorithm feeds multiple DIs with identical monitored images to a CNN-based model, which will concurrently analyze their features. Tests with CARABAS-II data show that the proposed CDAs can outperform other state-of-theart algorithms that also use stacks of WR-SAR images. Beyond that, the proposed algorithms outperformed a CNN-based CDA that does not use image stacks, which shows that CNN-based algorithms can use the additional information provided by stacks of SAR images to reduce false alarm occurrences while increasing the probability of detection of changes.

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Wavelength-Resolution SAR Change Detection Using Bayes’ Theorem

Cited by 13 publications

References 25 publications

Target Detection Method for Intensity VHF Wavelength-Resolution SAR Images

Target Detection Method for Intensity VHF Wavelength-Resolution SAR Images

Change detection method for intensity VHF wavelength-resolution SAR images

Change Detection Based on Convolutional Neural Networks Using Stacks of Wavelength-Resolution Synthetic Aperture Radar Images

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