Abstract
Remote sensing image registration can benefit from a machine learning method based on the likelihood of predicting semantic spatial position distributions. Semantic segmentation of images has been revolutionized due to the accessibility of high-resolution remote sensing images and the advancement of machine learning techniques. This system captures the semantic distribution location of the matching reference picture, which ML mapped using learning-based algorithms. The affine invariant is utilized to determine the semantic template’s barycenter position and the pixel’s center, which changes the semantic border alignment problem into a point-to-point matching issue for the machine learning-based semantic pattern matching (ML-SPM) model. The first step examines how various factors such as template radius, training label filling form, or loss function combination affect matching accuracy. In this second step, the matching of sub-images (MSI) images is compared using heatmaps created from the expected similarity between the images’ cropped sub-images. Images having radiometric discrepancies are matched with excellent accuracy by the approach. SAR-optical image matching has never been easier, and now even large-scale sceneries can be registered using this approach, which is a significant advance over previous methods. Optical satellite imaging or multi-sensor stereogrammetry can be combined with both forms of data to enhance geolocation.
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References
Banasiak, P. Z., Berezowski, P. L., Zapłata, R., Mielcarek, M., Duraj, K., & Stereńczak, K. (2022). Semantic segmentation (U-Net) of archaeological features in airborne laser scanning—example of the białowieża forest. Remote Sensing, 14(4), 995.
Cai, Y., Yang, Y., Zheng, Q., Shen, Z., Shang, Y., Yin, J., & Shi, Z. (2022). BiFDANet: Unsupervised bidirectional domain adaptation for semantic segmentation of remote sensing images. Remote Sensing, 14(1), 190.
Cao, Z., Diao, W., Sun, X., Lyu, X., Yan, M., & Fu, K. (2021). C3Net: Cross-modal feature recalibrated, cross-scale semantic aggregated and compact network for semantic segmentation of multimodal high-resolution aerial images. Remote Sensing, 13(3), 528.
Chen, F., Liu, H., Zeng, Z., Zhou, X., & Tan, X. (2022). BES-Net: Boundary enhancing semantic context network for high-resolution image semantic segmentation. Remote Sensing, 14(7), 1638.
Chen, G., Tan, X., Guo, B., Zhu, K., Liao, P., Wang, T., Wang, Q., & Zhang, X. (2021b). SDFCNv2: An improved FCN framework for remote sensing images semantic segmentation. Remote Sensing, 13(23), 4902.
Chen, Z., Li, D., Fan, W., Guan, H., Wang, C., & Li, J. (2021a). Self-attention in reconstruction bias U-Net for semantic segmentation of building rooftops in optical remote sensing images. Remote Sensing, 13(13), 2524.
Colin, A., Fablet, R., Tandeo, P., Husson, R., Peureux, C., Longépé, N., & Mouche, A. (2022). Semantic segmentation of metoceanic processes using SAR observations and deep learning. Remote Sensing, 14(4), 851.
Du, S., Du, S., Liu, B., & Zhang, X. (2021). Mapping large-scale and fine-grained urban functional zones from VHR images using a multi-scale semantic segmentation network and object based approach. Remote Sensing of Environment, 261, 112480.
Fu, H., Fu, B., & Shi, P. (2021). An improved segmentation method for automatic mapping of cone karst from remote sensing data based on deeplab V3+ model. Remote Sensing, 13(3), 441.
He, Y., Wang, J., Liao, C., Shan, B., & Zhou, X. (2022). ClassHyPer: ClassMix-based hybrid perturbations for deep semi-supervised semantic segmentation of remote sensing imagery. Remote Sensing, 14(4), 879.
https://www.kaggle.com/datasets/apollo2506/landuse-scene-classification
Li, J., Huang, S., Cui, H., Ma, Y., & Chen, X. (2021b). Automatic point cloud registration for large outdoor scenes using a priori semantic information. Remote Sensing, 13(17), 3474.
Li, L., Han, L., Ding, M., Cao, H., & Hu, H. (2021a). A deep learning semantic template matching framework for remote sensing image registration. ISPRS Journal of Photogrammetry and Remote Sensing, 181, 205–217.
Li, P., Zhang, D., Wulamu, A., Liu, X., & Chen, P. (2021d). Semantic relation model and dataset for remote sensing scene understanding. ISPRS International Journal of Geo-Information, 10(7), 488.
Li, X., Li, T., Chen, Z., Zhang, K., & Xia, R. (2021e). Attentively learning edge distributions for semantic segmentation of remote sensing imagery. Remote Sensing, 14(1), 102.
Li, Y., Shi, T., Zhang, Y., Chen, W., Wang, Z., & Li, H. (2021c). Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 20–33.
Makarichev, V., Vasilyeva, I., Lukin, V., Vozel, B., Shelestov, A., & Kussul, N. (2021). Discrete atomic transform-based lossy compression of three-channel remote sensing images with quality control. Remote Sensing, 14(1), 125.
Seong, S., & Choi, J. (2021). Semantic segmentation of urban buildings using a high-resolution network (HRNet) with channel and spatial attention gates. Remote Sensing, 13(16), 3087.
Su, Y., Zhong, Y., Zhu, Q., & Zhao, J. (2021). Urban scene understanding based on semantic and socioeconomic features: From high-resolution remote sensing imagery to multi-source geographic datasets. ISPRS Journal of Photogrammetry and Remote Sensing, 179, 50–65.
Sun, X., Xia, M., & Dai, T. (2022). Controllable fused semantic segmentation with adaptive edge loss for remote sensing parsing. Remote Sensing, 14(1), 207.
Wang, Z., Wang, J., Yang, K., Wang, L., Su, F., & Chen, X. (2022). Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+. Computers & Geosciences, 158, 104969.
Wang, Z., Yang, P., Liang, H., Zheng, C., Yin, J., Tian, Y., & Cui, W. (2021). Semantic segmentation and analysis on sensitive parameters of forest fire smoke using smoke-Unet and landsat-8 imagery. Remote Sensing, 14(1), 45.
Wei, P., Chai, D., Lin, T., Tang, C., Du, M., & Huang, J. (2021). Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model. ISPRS Journal of Photogrammetry and Remote Sensing, 174, 198–214.
Yan, L., Huang, J., Xie, H., Wei, P., & Gao, Z. (2022). Efficient depth fusion transformer for aerial image semantic segmentation. Remote Sensing, 14(5), 1294.
Yang, X., Li, S., Chen, Z., Chanussot, J., Jia, X., Zhang, B., Li, B., & Chen, P. (2021). An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 177, 238–262.
Zheng, Y., Yang, M., Wang, M., Qian, X., Yang, R., Zhang, X., & Dong, W. (2022). Semi-supervised adversarial semantic segmentation network using transformer and multiscale convolution for high-resolution remote sensing imagery. Remote Sensing, 14(8), 1786.
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Jaber, M.M., Ali, M.H., Abd, S.K. et al. A Machine Learning-Based Semantic Pattern Matching Model for Remote Sensing Data Registration. J Indian Soc Remote Sens 50, 2303–2316 (2022). https://doi.org/10.1007/s12524-022-01604-w
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DOI: https://doi.org/10.1007/s12524-022-01604-w