Abstract
Monitoring a specific area to analyze a continuous change has become more accessible by using optical images in remote sensing technology. However, several natural and artificial aspects such as fog and air pollution make it difficult to extract correct geometric information. To overcome the limitation of optical images, Synthetic Aperture Radar (SAR) images can be used to access more accurate information with respect to the targeted area. In this manner, optical and SAR images can be utilized together to detect the scale of change even in bad weather conditions. To process optical and SAR images, an image translation process-oriented Deep Adaptation-based Change Detection Technique (DACDT) is proposed. An optimized U-Net++ model is proposed that helps to improve the global and regional impacts of the images. Moreover, a multi-scale loss function is utilized to access the features of different dimensions. In this manner, the final change maps are generated by transferring the features of optical images to the SAR images for better change analysis. The prediction performance of the proposed approach is evaluated on four different datasets such as Gloucester I, Shuguang Village, Gloucester-II, and California. The calculated outcomes define the prediction performance of the proposed solution by registering the accuracy of 98.67%, 99.77%, 97.68%, and 98.87%, respectively.
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Data Availability Statements
The datasets generated during and/or analysed during the current study are available in the [RSL] repository, https://sites.google.com/michelevolpiresearch/codes/cross-sensor
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Alcantarilla PF, Stent S, Ros G, Arroyo R (2018) Street-view change detection with deconvolutional networks. Auton Robot 42(7):1301–1322
Ao D, Dumitru CO, Schwarz G, Datcu M (2018) Dialectical GAN for SAR image translation: From Sentinel-1 to TerraSAR-X. Remote Sens 10 (10):1597
Asokan A (2019) Change detection techniques for remote sensing applications: a survey. Earth Sci Inf 12(2):143–160
Ayhan B, Kwan C (2019) A new approach to change detection using heterogeneous images. In: 2019 IEEE 10th annual ubiquitous computing, electronics & mobile communication conference (UEMCON) (pp 0192–0197) IEEE
Chen R, Huang W, Huang B, Sun F, Fang B (2020) Reusing discriminators for encoding: Towards unsupervised image-to-image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp 8168–8177
Chollet F (2017) Xception: Deep learning with depthwise separable convolutions
Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Review ArticleDigital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25(9):1565–1596
de Boer PT, kroese D, Mannor S, Rubinstein R (2005) A tutorial on the cross-entropy method. Ann Oper Res 134(1):19
Dellinger F, Delon J, Gousseau Y, Michel J, Tupin F (2014) Change detection for high resolution satellite images, based on SIFT descriptors and an a contrario approach. In: 2014 IEEE Geoscience and remote sensing symposium (pp 1281–1284) IEEE
Deng J, Huang Y, Chen B, Tong C, Liu P, Wang H, Hong Y (2019) A methodology to monitor urban expansion and green space change using a time series of multi-sensor SPOT and sentinel-2A images. Remote Sens 11(10):1230
Geng J, Ma X, Zhou X (2019) Saliency-guided deep neural networks for SAR image change detection. IEEE Trans Geosci Remote Sens 57(10):7365–7377
Giustarini L, Hostache R, Matgen P, Schumann GJP, Bates PD (2012) A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Trans Geosci Remote Sens 51(4):2417–2430
Hertzmann A, Jacobs CE, Oliver N, Curless B (2001) DH, Salesin Image analogies SIGGRAPH
Hou B, Liu Q, Wang H (2019) From W-Net to CDGAN: Bitemporal change detection via deep learning techniques. IEEE Trans Geosci Remote Sens 58(3):1790–1802
Isola P, Zhu JY, Zhou T, Efros A (2017) A Image-to-image translation with conditional adversarial networks. In: proceedings of the IEEE conference on computer vision and pattern recognition. pp 1125–1134
Lee K, Xu W, Fan F, Tu Z (2018) Wasserstein introspective neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 3702–3711
Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: proceedings of the IEEE conference on computer vision and pattern recognition. pp 2117–2125
Liu Z, Li G, Mercier G, He Y (2017) Change detection in heterogenous remote sensing images via homogeneous pixel transformation. IEEE Trans Image Process 27(4):1822–1834
Longbotham N, Pacifici F, Glenn T, Zare A, Volpi M, Tuia D (2012) Multi-modal change detection, application to the detection of flooded areas: Outcome of the 2009–2010 data fusion contest. IEEE Journal of selected topics in applied earth observations and remote sensing 5(1):331–342
Luppino LT, Bianchi FM, Moser G (2019) Unsupervised image regression for heterogeneous change detection. arXiv:1909.05948
Ma C, Huang JB, Yang X, Yang MH (2015) Hierarchical convolutional features for visual tracking. In: proceedings of the IEEE international conference on computer vision, pp 3074–3082
Mercier G, Moser G (2008) Conditional copulas for change detection in heterogeneous remote sensing images. IEEE Trans Geosci Remote Sens 46(5):1428–1441
Mignotte M (2020) A fractal projection and Markovian segmentation-based approach for multimodal change detection. IEEE Trans Geosci Remote Sens 58 (11):8046–8058
Mubea K (2012) Monitoring land-use change in Nakuru (Kenya) using multi-sensor satellite data
Peng D, Zhang Y (2019) End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sens 11(11):1382
Planinšič P (2018) Temporal change detection in SAR images using log cumulants and stacked autoencoder. IEEE Geosci Remote Sens Lett 15(2):297–301
Prendes J, Chabert M, Pascal F, Giros A (2014) A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors. IEEE Trans Image Process 24(3):799–812
Rahman M, Islam M, Sassi R (2019) Convolutional neural networks performance comparison for handwritten Bengali numerals recognition. SN Appl Sci 1 (12):1–11
Saha S, Bovolo F, Bruzzone L (2018) Destroyed-buildings detection from VHR SAR images using deep features. In: Image and signal processing for remote sensing XXIV (vol 10789, pp 107890Z). international society for optics and photonics
Shang R, He J, Wang J, Xu K, Jiao L, Stolkin R (2020) Dense connection and depthwise separable convolution based CNN for polarimetric SAR image classification. Knowl-Based Syst 194:105542
Shi Q, Liu M, Liu X, Liu P, Zhang P, Yang J, Li X (2019) Domain adaption for fine-grained urban village extraction from satellite images. IEEE Geosci Remote Sens Lett 17(8):1430–1434
Shi Q, Liu M, Li S, Liu X, Zhang L, Wang F (2021) A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection. IEEE Transactions on Geoscience and Remote Sensing
Singh A (1989) Review article digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989–1003
Sun Y, Lei L, Li X, Tan X (2020) Patch similarity graph matrix-based unsupervised remote sensing change detection with homogeneous and heterogeneous sensors. IEEE Trans Geosci Remote Sens 59(6):4841–4861
Sun Y, Lei L, Li X, Tan X, Kuang G (2021) Structure consistency-based graph for unsupervised change detection with homogeneous and heterogeneous remote sensing images. IEEE transactions on geoscience and remote sensing
Touati R, Mignotte M (2019) Multimodal change detection in remote sensing images using an unsupervised pixel pairwise-based Markov random field model. IEEE Trans Image Process 29:757–767
Turnes JN, Castro JDB, Torres DL, Vega PJS, Feitosa RQ, Happ PN (2020) Atrous cGAN for SAR to Optical Image Translation. IEEE Geoscience and Remote Sensing Letters
Wan L, Xiang Y (2019) A post-classification comparison method for SAR and optical images change detection. IEEE Geosci Remote Sens Lett 16(7):1026–1030
Wang X, Qiu S, Liu K, Tang X (2013) Web image re-ranking usingquery-specific semantic signatures. IEEE Trans Pattern Anal Mach Intell 36(4):810–823
Wang Y, Gao L, Hong D, Sha J, Liu L, Zhang B, Zhang Y (2021) Mask DeepLab: End-to-end image segmentation for change detection in high-resolution remote sensing images. Int J Appl Earth Obs Geoinf 104:102582
Wu F, Jing XY, Dong X, Hu R, Yue D, Wang L, Chen G (2018) Intraspectrum discrimination and interspectrum correlation analysis deep network for multispectral face recognition. IEEE Trans Cybern 50(3):1009–1022
Wu F, Dong X, Han L, Jing XY, Ji YM (2019) Multi-view synthesis and analysis dictionaries learning for classification. IEICE Trans Info Syst 102(3):659–662
Xiong J, Lin C, Ma R, Cao Z (2019) Remote sensing estimation of lake total phosphorus concentration based on MODIS: a case study of Lake Hongze. Remote Sens 11(17):2068
Zhu JY, Park T, Isola P, Efros A (2017) A Unpaired image-to-image translation using cycle-consistent adversarial networks. In: proceedings of the IEEE international conference on computer vision. pp 2223–2232
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support (pp 3–11). Springer, Cham
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Manocha, A., Afaq, Y. Optical and SAR images-based image translation for change detection using generative adversarial network (GAN). Multimed Tools Appl 82, 26289–26315 (2023). https://doi.org/10.1007/s11042-023-14331-2
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DOI: https://doi.org/10.1007/s11042-023-14331-2