Evaluation of the change in synthetic aperture radar imaging using transfer learning and residual network

Evaluation of the change in synthetic aperture radar imaging using transfer learning and residual network

Автор: Hamdi Imad, Tounsi Yassine, Benjelloun Mohammed, Nassim Abdelkrim

Журнал: Компьютерная оптика @computer-optics

Рубрика: Обработка изображений, распознавание образов

Статья в выпуске: 4 т.45, 2021 года.

Бесплатный доступ

Change detection from synthetic aperture radar images becomes a key technique to detect change area related to some phenomenon as flood and deformation of the earth surface. This paper proposes a transfer learning and Residual Network with 18 layers (ResNet-18) architecture-based method for change detection from two synthetic aperture radar images. Before the application of the proposed technique, batch denoising using convolutional neural network is applied to the two input synthetic aperture radar image for speckle noise reduction. To validate the performance of the proposed method, three known synthetic aperture radar datasets (Ottawa; Mexican and for Taiwan Shimen datasets) are exploited in this paper. The use of these datasets is important because the ground truth is known, and this can be considered as the use of numerical simulation. The detected change image obtained by the proposed method is compared using two image metrics. The first metric is image quality index that measures the similarity ratio between the obtained image and the image of the ground truth, the second metrics is edge preservation index, it measures the performance of the method to preserve edges. Finally, the method is applied to determine the changed area using two Sentinel 1 B synthetic aperture radar images of Eddahbi dam situated in Morocco.

Еще

Sar images, change detection, transfer learning, residual network

Короткий адрес: https://sciup.org/140290255

IDR: 140290255   |   DOI: 10.18287/2412-6179-CO-814

Список литературы Evaluation of the change in synthetic aperture radar imaging using transfer learning and residual network

  • Bindschadler RA, Jezek KC, Crawford J. Glaciological investigations using the synthetic aperture radar imaging system. Ann Glaciol 1987; 9: 11-19. DOI: 10.1017/S0260305500000318.
  • Valenzuela GR. An asymptotic formulation for SAR images of the dynamical ocean surface. Radio Sci 1980; 15(1): 105-114. DOI: 10.1029/RS015i001p00105.
  • Yang J, Sun W. Automatic analysis of the slight change image for unsupervised change detection. JARS 2015; 9(1): 095995. DOI: 10.1117/1.JRS.9.095995.
  • Mu C-H, Li C-Z, Liu Y, Qu R, Jiao L-C. Accelerated genetic algorithm based on search-space decomposition for change detection in remote sensing images. Appl Soft Comput 2019; 84: 105727. DOI: 10.1016/j.asoc.2019.105727.
  • Mu C-H, Li C-Z, Liu Y, Qu R, Jiao L-C. Accelerated genetic algorithm based on search-space decomposition for change detection in remote sensing images. Appl Soft Comput 2019; 84: 105727. DOI: 10.1016/j.asoc.2019.105727.
  • Li H, Gong M, Wang Q, Liu J, Su L. A multiobjective fuzzy clustering method for change detection in SAR images. Appl Soft Comput 2016; 46: 767-777. DOI: 10.1016/j.asoc.2015.10.044.
  • Mishra NS, Ghosh S, Ghosh A. Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images. Appl Soft Comput 2012; 12(8): 2683-2692. DOI: 10.1016/j.asoc.2012.03.060.
  • Zhuang H, Fan H, Deng K, Yu Y. An improved neighborhood-based ratio approach for change detection in SAR images. Eur J Remote Sens 2018; 51(1): 723-738.
  • White RG. Change detection in SAR imagery. Int J Remote Sens 1991; 12(2): 339-360. DOI: 10.1080/01431169108929656.
  • Bao M. Backscattering change detection in SAR images using wavelet techniques. IEEE 1999 International Geosci-ence and Remote Sensing Symposium (IGARSS'99) 1999; 3: 1561-1563. DOI: 10.1109/IGARSS.1999.772019.
  • Mu C, Li C, Liu Y, Sun M, Jiao L, Qu R. Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm. 2017 IEEE Congress on Evolutionary Computation (CEC) 2017: 1150-1157. DOI: 10.1109/CEC.2017.7969436.
  • Wenyan Z, Zhenhong J, Yu Y, Yang J, Kasabov N. SAR image change detection based on equal weight image fusion and adaptive threshold in the NSST domain. Eur J Remote Sens 2018; 51(1): 785-794. DOI: 10.1080/22797254.2018.1491804.
  • Gao F, Wang X, Gao Y, Dong J, Wang S. Sea ice change detection in SAR images based on convolutional-wavelet neural networks. IEEE Geosci Remote Sens Lett 2019; 16(8): 1240-1244. DOI: 10.1109/LGRS.2019.2895656.
  • Li Y, Peng C, Chen Y, Jiao L, Zhou L, Shang R. A deep learning method for change detection in synthetic aperture radar images. IEEE Trans Geosci Remote Sens 2019; 57(8): 5751-5763. DOI: 10.1109/TGRS.2019.2901945.
  • Gao F, Dong J, Li B, Xu Q, Xie C. Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine. JARS 2016; 10(4): 046019. DOI: 10.1117/1.JRS.10.046019.
  • Gao Y, Gao F, Dong J, Wang S. Change detection from synthetic aperture radar images based on channel weighting-based deep cascade network. IEEE J Sel Top Appl Earth Obs Remote Sens 2019; 12(11): 4517-4529. DOI: 10.1109/JSTARS.2019.2953128.
  • Imad H, Yassine T, Mohammed B, Abdelkrim N. Batch despeckling of SAR images by a convolutional neural network-based method. 2020 IEEE International Conference of Moroccan Geomatics (Morgeo) 2020: 1-6. DOI: 10.1109/Morgeo49228.2020.9121890.
  • He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV) 2017: 2980-2988. DOI: 10.1109/ICCV.2017.322.
  • He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. arXiv preprint 2020. Source: (http://arxiv.org/abs/1512.03385).
  • Napoletano P, Piccoli F, Schettini R. Anomaly detection in nanofibrous materials by CNN-based self-similarity. Sensors 2018; 18(1): 1. doi: 10.3390/s18010209.
  • Tounsi Y, Kumar M, Nassim A, Mendoza-Santoyo F, Matoba O. Speckle denoising by variant nonlocal means methods. Appl Opt 2019; 58(26): 7110-7120. DOI: 10.1364/AO.58.007110.
  • Wang Z, Bovik AC. A universal image quality index. IEEE Signal Process Lett 2002; 9(3): 81-84. DOI: 10.1109/97.995823.
  • Gong M, Zhou Z, Ma J. Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process 2012; 21(4): 2141-2151. DOI: 10.1109/TIP.2011.2170702.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp