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Change Detection Based on Convolutional Neural Networks Using Stacks of Wavelength-Resolution Synthetic Aperture Radar Images
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- Vinholi, João G. (author)
- Aeronautics Institute of Technology (ITA), São José dos Campos, BRA
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- Palm, Bruna (author)
- Blekinge Tekniska Högskola,Institutionen för matematik och naturvetenskap
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- Silva, Danilo (author)
- Federal University of Santa Catarina (UFSC), Florianopolis, BRA
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- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- English.
- In: IEEE Transactions on Geoscience and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 0196-2892 .- 1558-0644. ; 60
- Journal article (peer-reviewed)
Abstract
Subject headings
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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-the-art 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Keyword
- Change detection
- Convolution
- Deep learning
- Image enhancement
- Neural networks
- Radar imaging
- Remote sensing
- Semantic Segmentation
- Semantics
- Signal detection
- Tracking radar
- CARABAS
- CARABAS-II
- Complexity theory
- Convolutional neural network
- Detection algorithm
- Radar polarimetry
- Remote-sensing
- Ultra-wideband technology
- Synthetic aperture radar
- Classification
- CNN
- Convolutional neural networks
- Detection algorithms
- Monitoring
- Ultra wideband technology
Publication and Content Type
- ref (subject category)
- art (subject category)
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